Lung cancer CNN detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "headed-release",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Intro\" data-toc-modified-id=\"Intro-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Intro</a></span></li><li><span><a href=\"#Lung-cancer-dataset\" data-toc-modified-id=\"Lung-cancer-dataset-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Lung cancer dataset</a></span></li><li><span><a href=\"#Model\" data-toc-modified-id=\"Model-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Model</a></span></li><li><span><a href=\"#Results\" data-toc-modified-id=\"Results-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Results</a></span></li><li><span><a href=\"#Wrongly-classified-samples\" data-toc-modified-id=\"Wrongly-classified-samples-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Wrongly classified samples</a></span></li><li><span><a href=\"#Results-discussion\" data-toc-modified-id=\"Results-discussion-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Results discussion</a></span></li><li><span><a href=\"#ResNet50\" data-toc-modified-id=\"ResNet50-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>ResNet50</a></span></li><li><span><a href=\"#References-and-links\" data-toc-modified-id=\"References-and-links-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>References and links</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535e9a99",
   "metadata": {},
   "source": [
    "# Intro\n",
    "This notebook explores Lung and Colon Cancer Histopathological Image Dataset (LC25000) with the focus on lung cancer images.  only. Motivation behind the database is that together, Lung and Colon cancers are the two most common causes of cancer deaths in the United States, as author states. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "dominant-melbourne",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, Dropout, Add\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.pooling import MaxPooling2D, AveragePooling2D\n",
    "from keras.layers.core import Flatten, Dense\n",
    "from keras.utils import np_utils\n",
    "#from keras.initializers import glorot_uniform\n",
    "from keras import backend as K\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e7a7521e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for sure if channels should be last - it was changing due to some import\n",
    "K.image_data_format() == 'channels_first'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5e88f8",
   "metadata": {},
   "source": [
    "# Lung cancer dataset\n",
    "\n",
    "The dataset contains 25,000 color images with five classes of 5,000\n",
    "images each. All images are 768 x 768 pixels in size and are in jpeg\n",
    "file format. The main folder\n",
    "lung_colon_image_set contains two subfolders: colon_image_sets\n",
    "and lung_image_sets. \n",
    "\n",
    "The subfolder colon_image_sets contains two secondary subfolders: \n",
    "- colon_aca subfolder with 5,000 images of colon adenocarcinomas \n",
    "- colon_n subfolder with 5,000 images of benign colonic tissues. \n",
    "\n",
    "\n",
    "The subfolder lung_image_sets contains three secondary subfolders: \n",
    "- lung_aca subfolder with 5,000 images of lung adenocarcinomas \n",
    "- lung_scc subfolder with 5,000 images of lung squamous cell carcinomas \n",
    "- lung_n subfolder with 5,000 images of benign lung tissues.\n",
    "\n",
    "Here I use only lung tissues images. The images are huge so I resize them right away to 80x80x3 size. Not to worry about imbalanced classes here - authors of database took care of them having same number of samples. Important thing to note is that some of the pictures come from augmentation as authors mentioned it in the paper https://arxiv.org/ftp/arxiv/papers/1912/1912.12142.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c8782e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "\n",
    "def load_images(folder):\n",
    "    images = []\n",
    "    for filename in os.listdir(folder):\n",
    "        img = cv2.imread(os.path.join(folder, filename))\n",
    "        if img is not None:\n",
    "            img = cv2.resize(img, (80,80))\n",
    "            images.append(img)\n",
    "            \n",
    "    return np.array(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32ba861b",
   "metadata": {},
   "outputs": [],
   "source": [
    "benign = load_images('./lung_colon_image_set/lung_image_sets/lung_n/')\n",
    "mal_aca = load_images('./lung_colon_image_set/lung_image_sets/lung_aca/')\n",
    "mal_scc = load_images('./lung_colon_image_set/lung_image_sets/lung_scc/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91514995",
   "metadata": {},
   "source": [
    "Every class has equal number of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "a4f39232",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images for every class: BENIGN 5000, ADENOCARCINOMAS 5000, SQUAMOS CELL CARCINOMAS 5000.\n",
      "Images shape: (80, 80, 3).\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of images for every class: BENIGN {benign.shape[0]}, ADENOCARCINOMAS {mal_aca.shape[0]}, SQUAMOS CELL CARCINOMAS {mal_scc.shape[0]}.\")\n",
    "\n",
    "print(f\"Images shape: {benign[0].shape}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "2da50940",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indices = [0, 40, 2300]\n",
    "\n",
    "plt.figure(1, figsize=(15,5))\n",
    "plt.grid(None)\n",
    "\n",
    "for n, idx in enumerate(indices):\n",
    "    plt.subplot(n+1, 3, 1) \n",
    "    plt.imshow(benign[idx])\n",
    "    plt.title('benign')\n",
    "    plt.subplot(n+1, 3, 2) \n",
    "    plt.imshow(mal_aca[idx])\n",
    "    plt.title('malignant aca')\n",
    "    plt.subplot(n+1, 3, 3) \n",
    "    plt.imshow(mal_scc[idx])\n",
    "    plt.title('malignant scc')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042a109d",
   "metadata": {},
   "source": [
    "Make datasets - concatenate images from three classes and create labels for them. Samples shape should be 15000x80x80x3, labels 15000. They are stacked together so next step is shuffling both of them randomly. Important thing to normalize pictures as usual to [0,1] range by dividing them by 255 (the pixel values can range from 0 to 256). When using the image as it is and passing through a deep neural network, the computation of high numeric values would be more complex and slower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5775062d",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = np.concatenate((benign, mal_aca, mal_scc))\n",
    "labels = np.array(benign.shape[0] * [0] + mal_aca.shape[0] * [1] + mal_scc.shape[0] * [2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "1b5d15a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples shape check: (15000, 80, 80, 3).\n",
      "Labels shape check: (15000,).\n"
     ]
    }
   ],
   "source": [
    "print(f\"Samples shape check: {samples.shape}.\")\n",
    "print(f\"Labels shape check: {labels.shape}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6656ea21",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.arange(samples.shape[0])\n",
    "np.random.shuffle(indices)\n",
    "\n",
    "samples = samples[indices]\n",
    "labels = labels[indices]\n",
    "\n",
    "# normalize pictures\n",
    "samples = samples.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad90c7e6",
   "metadata": {},
   "source": [
    "Split data. Here 15000 samples are in fact not so many so standard 80/20 division is introduced. From test data, which is 20%, validation set is further obtained by splitting it 50/50. So train set is 80% of images, validation set is 10% and test set is 10%. \n",
    "\n",
    "Labels are one hot encoded to be categorical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b3dace99",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size = 0.2)\n",
    "\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "074ed655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 0.]\n",
      "Y_train shape after one hot encoding: (1500, 3)\n",
      "Y_train shape after one hot encoding: (1500, 80, 80, 3)\n"
     ]
    }
   ],
   "source": [
    "Y_train = np_utils.to_categorical(y_train, 3)\n",
    "Y_val = np_utils.to_categorical(y_val, 3)\n",
    "Y_test = np_utils.to_categorical(y_test, 3)\n",
    "\n",
    "print(Y_train[0])\n",
    "print(f\"Y_train shape after one hot encoding: {Y_val.shape}\")\n",
    "print(f\"Y_train shape after one hot encoding: {X_val.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bce9edb",
   "metadata": {},
   "source": [
    "# Model\n",
    "CNN is used to test image classification consisting of input layer, three hidden conv layers with filter 3x3 each followed by maxpooling with filter 2x2 and dropout 0.2, one fully connected layer and one output layer with 3 units and softmax activation function. For hidden layers relu was used as activation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "cda4e45c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def base_model(input_shape=(80,80,3) classes=3):\n",
    "    '''\n",
    "    CNN base model with 4 hidden layers (3 conv and one fully connected)\n",
    "    '''\n",
    "    inputs = Input(shape=input_shape)\n",
    "\n",
    "    X = Conv2D(32, 3, 3, activation='relu')(inputs)\n",
    "    X = MaxPooling2D(pool_size=(2,2))(X)\n",
    "\n",
    "    X = Conv2D(64, 3, 3, activation='relu')(X)\n",
    "    X = MaxPooling2D(pool_size=(2,2))(X)\n",
    "    X = Dropout(0.2)(X)\n",
    "\n",
    "    X = Conv2D(128, 3, 3, activation='relu', padding='same')(X)\n",
    "    X = MaxPooling2D(pool_size=(2,2), padding='same')(X)\n",
    "    X = Dropout(0.2)(X)\n",
    "\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(256, activation='relu')(X)\n",
    "    outputs = Dense(classes, activation='softmax')(X)\n",
    "\n",
    "    model = Model(inputs, outputs)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "b76aee88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_22\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_65 (InputLayer)        [(None, 80, 80, 3)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d_190 (Conv2D)          (None, 26, 26, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_139 (MaxPoolin (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_191 (Conv2D)          (None, 4, 4, 64)          18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_140 (MaxPoolin (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_152 (Dropout)        (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_192 (Conv2D)          (None, 1, 1, 128)         73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_141 (MaxPoolin (None, 1, 1, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_153 (Dropout)        (None, 1, 1, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_29 (Flatten)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 256)               33024     \n",
      "_________________________________________________________________\n",
      "dense_45 (Dense)             (None, 3)                 771       \n",
      "=================================================================\n",
      "Total params: 127,043\n",
      "Trainable params: 127,043\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = base_model(input_shape=(80,80,3) classes=3)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "881d6e9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "d34661c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "188/188 [==============================] - 9s 42ms/step - loss: 0.7119 - accuracy: 0.7631 - val_loss: 0.3177 - val_accuracy: 0.8647\n",
      "Epoch 2/25\n",
      "188/188 [==============================] - 7s 39ms/step - loss: 0.3567 - accuracy: 0.8456 - val_loss: 0.2540 - val_accuracy: 0.8953\n",
      "Epoch 3/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.3168 - accuracy: 0.8703 - val_loss: 0.2481 - val_accuracy: 0.8993\n",
      "Epoch 4/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.2761 - accuracy: 0.8832 - val_loss: 0.2721 - val_accuracy: 0.8827\n",
      "Epoch 5/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.2547 - accuracy: 0.8938 - val_loss: 0.2531 - val_accuracy: 0.9073\n",
      "Epoch 6/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.2619 - accuracy: 0.8907 - val_loss: 0.2402 - val_accuracy: 0.8960\n",
      "Epoch 7/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.2352 - accuracy: 0.9038 - val_loss: 0.1876 - val_accuracy: 0.9287\n",
      "Epoch 8/25\n",
      "188/188 [==============================] - 7s 39ms/step - loss: 0.2493 - accuracy: 0.8967 - val_loss: 0.2772 - val_accuracy: 0.8740\n",
      "Epoch 9/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.2138 - accuracy: 0.9159 - val_loss: 0.1987 - val_accuracy: 0.9207\n",
      "Epoch 10/25\n",
      "188/188 [==============================] - 7s 39ms/step - loss: 0.2183 - accuracy: 0.9097 - val_loss: 0.2641 - val_accuracy: 0.9027\n",
      "Epoch 11/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.2124 - accuracy: 0.9118 - val_loss: 0.1830 - val_accuracy: 0.9220\n",
      "Epoch 12/25\n",
      "188/188 [==============================] - 8s 42ms/step - loss: 0.2008 - accuracy: 0.9181 - val_loss: 0.2062 - val_accuracy: 0.9047\n",
      "Epoch 13/25\n",
      "188/188 [==============================] - 8s 41ms/step - loss: 0.1959 - accuracy: 0.9205 - val_loss: 0.2286 - val_accuracy: 0.9000\n",
      "Epoch 14/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.1850 - accuracy: 0.9230 - val_loss: 0.1654 - val_accuracy: 0.9307\n",
      "Epoch 15/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.1884 - accuracy: 0.9224 - val_loss: 0.1712 - val_accuracy: 0.9307\n",
      "Epoch 16/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.1820 - accuracy: 0.9283 - val_loss: 0.1955 - val_accuracy: 0.9207\n",
      "Epoch 17/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.1728 - accuracy: 0.9274 - val_loss: 0.1444 - val_accuracy: 0.9400\n",
      "Epoch 18/25\n",
      "188/188 [==============================] - 9s 47ms/step - loss: 0.1690 - accuracy: 0.9302 - val_loss: 0.1855 - val_accuracy: 0.9273\n",
      "Epoch 19/25\n",
      "188/188 [==============================] - 8s 41ms/step - loss: 0.1676 - accuracy: 0.9309 - val_loss: 0.1780 - val_accuracy: 0.9260\n",
      "Epoch 20/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.1675 - accuracy: 0.9299 - val_loss: 0.1601 - val_accuracy: 0.9313\n",
      "Epoch 21/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.1573 - accuracy: 0.9354 - val_loss: 0.1558 - val_accuracy: 0.9333\n",
      "Epoch 22/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.1565 - accuracy: 0.9369 - val_loss: 0.1713 - val_accuracy: 0.9367\n",
      "Epoch 23/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.1652 - accuracy: 0.9317 - val_loss: 0.1841 - val_accuracy: 0.9267\n",
      "Epoch 24/25\n",
      "188/188 [==============================] - 7s 40ms/step - loss: 0.1561 - accuracy: 0.9368 - val_loss: 0.1730 - val_accuracy: 0.9307\n",
      "Epoch 25/25\n",
      "188/188 [==============================] - 8s 40ms/step - loss: 0.1537 - accuracy: 0.9360 - val_loss: 0.1725 - val_accuracy: 0.9307\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, batch_size=64, epochs=25, validation_data=(X_val, Y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ae92e6",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "445c18aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.figure(figsize=(16,7))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(history.history['loss'])  \n",
    "plt.plot(history.history['val_loss'])  \n",
    "plt.title('Model loss', fontsize=16)  \n",
    "plt.ylabel('Loss')  \n",
    "plt.xlabel('Epoch')  \n",
    "plt.legend(['train', 'val'])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(history.history['accuracy'])  \n",
    "plt.plot(history.history['val_accuracy'])  \n",
    "plt.title('Model accuracy', fontsize=16)  \n",
    "plt.ylabel('Accuracy')  \n",
    "plt.xlabel('Epoch')  \n",
    "plt.legend(['train', 'val'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "ae9a3a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "081ab353",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47/47 [==============================] - 0s 8ms/step\n",
      "375/375 [==============================] - 5s 12ms/step\n",
      "47/47 [==============================] - 1s 13ms/step - loss: 0.1669 - accuracy: 0.9287\n",
      "Train accuracy: 0.936\n",
      "Validation accuracy: 0.931\n",
      "Test accuracy: 0.929\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model.predict(X_test, verbose=1)\n",
    "y_pred_train = model.predict(X_train, verbose=1)\n",
    "\n",
    "# conversion to one hot encoding\n",
    "#y_pred_test = (y_pred_test > 0.5).astype(\"int32\")\n",
    "#y_pred_train = (y_pred_train > 0.5).astype(\"int32\")\n",
    "\n",
    "# convert it to numerical classes\n",
    "y_pred_test = np.argmax(y_pred_test, axis=1)\n",
    "y_pred_train = np.argmax(y_pred_train, axis=1)\n",
    "\n",
    "score_test = model.evaluate(X_test, Y_test)\n",
    "print(f\"Train accuracy: {history.history['accuracy'][-1]:.3f}\")\n",
    "print(f\"Validation accuracy: {history.history['val_accuracy'][-1]:.3f}\")\n",
    "print(f\"Test accuracy: {score_test[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "4b9dc0c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_names = ['benign', 'mal_aca', 'mal_scc']\n",
    "\n",
    "\n",
    "confmat = confusion_matrix(y_train, y_pred_train, normalize='true')\n",
    "sn.heatmap(confmat, annot=True, cmap='Blues', cbar=False, \n",
    "           xticklabels=label_names, yticklabels=label_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850f1d50",
   "metadata": {},
   "source": [
    "# Wrongly classified samples\n",
    "Here I've decided to explore some samples that model didn't get right. Most problems model had with mal_aca class, it mistook it for mal_scc. Run this cell few times to explore different examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "80c5ef7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mistakes = np.where(y_pred_train != y_train)[0]\n",
    "\n",
    "n = 5\n",
    "\n",
    "plt.figure(figsize=(5*n,5))\n",
    "\n",
    "# get 5 random false negatives\n",
    "for i, val in enumerate(np.random.choice(mistakes, n)):\n",
    "\n",
    "    plt.subplot(1,n,i+1)\n",
    "    plt.imshow(X_train[val])\n",
    "    plt.title(f\" {label_names[y_train[val]]} [pred:{label_names[y_pred_train[val]]}]\", fontsize=12)\n",
    "    plt.grid(None)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8294dba",
   "metadata": {},
   "source": [
    "# Results discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf761c33",
   "metadata": {},
   "source": [
    "Data seems to not be problematic, images are of high quality. However I wonder what human error would be on them. For me they certainly look different, for experienced doctor it wouldn't be a problem differentiating between benign and malignant I believe. Benign tissue doesn't have so much cells closely placed like in the malignant tissues and benign has far more red cells. About differentating one cancer (ACA) from another type (SCC) I'm not sure. SCC cells are more packed together and maybe are smaller than those in ACA images.\n",
    "\n",
    "\n",
    "I'm estimating human error to be around 10% for normal human and 4% for doctors (however this is wild assumptation, if any doctor magically happen to be reading this please contact me and tell me how easy it is for you). \n",
    "Therefore as human error aka proxy for Bayes error I would use 4%.\n",
    "\n",
    "With this assumption and training accuracy on 93.6% I can tell that I have a bias problem here. Difference between training and validation accuracy is quite low so still - bias is something I should focus on rather than variance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0819b04f",
   "metadata": {},
   "source": [
    "# ResNet50\n",
    "\n",
    "So I think that the CNN could definietly be deeper to reduce bias. In deeper layers model could explore differences between cancers that I don't know about. \n",
    "\n",
    "Here it is a bit extreme example - I use ResNet50, which is very deep (50 layers), following original architecture CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER. \n",
    "\n",
    "ResNet uses residual connections - for deep NN there is a problem with vanishing gradients. As you backprop from the final layer back to the first layer, you are multiplying by the weight matrix on each step, and thus the gradient can decrease exponentially quickly to zero. Residual connections are like shortcuts for activation functions through layers, they allow model to learn identity function very quickly. They prevent vanishing gradient problem and therefore doesn't hurt trainig performance.\n",
    "\n",
    "Two main types of blocks are used in a ResNet, depending mainly on whether the input/output dimensions are same (identity_block) or different (convolutional_block)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "11646e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "def identity_block(X, f, filters, stage, block):\n",
    "    \"\"\"\n",
    "    Implementation of the identity block \n",
    "    \n",
    "    Arguments:\n",
    "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
    "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
    "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
    "    stage -- integer, used to name the layers, depending on their position in the network\n",
    "    block -- string/character, used to name the layers, depending on their position in the network\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # defining name basis\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "    F1, F2, F3 = filters\n",
    "    \n",
    "    X_shortcut = X\n",
    "    \n",
    "\n",
    "    X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "\n",
    "    X = Conv2D(filters = F2, kernel_size = (f,f), strides = (1,1), padding='same', name= conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "\n",
    "    X = Conv2D(filters = F3, kernel_size = (1,1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis =3, name= bn_name_base + '2c')(X)\n",
    "\n",
    "    X = Add()([X,X_shortcut])\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "4a3adb00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convolutional_block(X, f, filters, stage, block, s = 2):\n",
    "    \"\"\"\n",
    "    Implementation of the convolutional block\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
    "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
    "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
    "    stage -- integer, used to name the layers, depending on their position in the network\n",
    "    block -- string/character, used to name the layers, depending on their position in the network\n",
    "    s -- Integer, specifying the stride to be used\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # defining name basis\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "    F1, F2, F3 = filters\n",
    "    \n",
    "    X_shortcut = X\n",
    "\n",
    "    X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    X = Conv2D(F2, (f,f), strides = (1,1), padding = 'same',name = conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    X = Conv2D(F3, (1,1), strides = (1,1), padding='valid', name = conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
    "\n",
    "\n",
    "    X_shortcut = Conv2D(F3, (1,1), strides = (s,s), padding='valid', name = conv_name_base + '1', kernel_initializer=glorot_uniform(seed=0))(X_shortcut)\n",
    "    X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)\n",
    "\n",
    "    X = Add()([X,X_shortcut])\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "3bbd0319",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResNet50(input_shape=(80, 80, 3), classes=3):\n",
    "    \"\"\"\n",
    "    Implementation of the popular ResNet50 the following architecture:\n",
    "    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3\n",
    "    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    X_input = Input(input_shape)\n",
    "\n",
    "    X = ZeroPadding2D((3, 3))(X_input)\n",
    "    \n",
    "\n",
    "    X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)\n",
    "    X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    X = MaxPooling2D((3, 3), strides=(2, 2))(X)\n",
    "\n",
    "\n",
    "    X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)\n",
    "    X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')\n",
    "    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')\n",
    "\n",
    "\n",
    "    X = convolutional_block(X, 3, [128,128,512], stage=3, s=2, block='a')\n",
    "    X = identity_block(X, 3, [128,128,512], stage=3, block='b')\n",
    "    X = identity_block(X, 3, [128,128,512], stage=3, block='c')\n",
    "    X = identity_block(X, 3, [128,128,512], stage=3, block='d')\n",
    "\n",
    "    X = convolutional_block(X, 3, [256,256,1024], stage=4, s=2, block='a')\n",
    "    X = identity_block(X, 3, [256,256,1024], stage=4, block='b')\n",
    "    X = identity_block(X, 3, [256,256,1024], stage=4, block='c')\n",
    "    X = identity_block(X, 3, [256,256,1024], stage=4, block='d')\n",
    "    X = identity_block(X, 3, [256,256,1024], stage=4, block='e')    \n",
    "\n",
    "  \n",
    "    X = convolutional_block(X, 3, [512,512,2048], stage=5, s=2, block='a')\n",
    "    X = identity_block(X, 3, [512,512,2048], stage=5, block='b')\n",
    "    X = identity_block(X, 3, [512,512,2048], stage=5, block='c')\n",
    "\n",
    "  \n",
    "    X = AveragePooling2D(pool_size=(2, 2), name=\"avg_pool\")(X)\n",
    "    \n",
    "    # output layer\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)\n",
    "    \n",
    "    \n",
    "    # Create model\n",
    "    model = Model(inputs = X_input, outputs = X, name='ResNet50')\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "8d116606",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.initializers import glorot_uniform\n",
    "resmodel = ResNet50(input_shape=(80, 80, 3), classes=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "a6f5f156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"ResNet50\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_56 (InputLayer)           [(None, 80, 80, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_6 (ZeroPadding2D (None, 86, 86, 3)    0           input_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 40, 40, 64)   9472        zero_padding2d_6[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 40, 40, 64)   256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_209 (Activation)     (None, 40, 40, 64)   0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_117 (MaxPooling2D (None, 19, 19, 64)   0           activation_209[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 19, 19, 64)   4160        max_pooling2d_117[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 19, 19, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_210 (Activation)     (None, 19, 19, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 19, 19, 64)   36928       activation_210[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 19, 19, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_211 (Activation)     (None, 19, 19, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 19, 19, 256)  16640       activation_211[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 19, 19, 256)  16640       max_pooling2d_117[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 19, 19, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 19, 19, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_70 (Add)                    (None, 19, 19, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_212 (Activation)     (None, 19, 19, 256)  0           add_70[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 19, 19, 64)   16448       activation_212[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 19, 19, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_213 (Activation)     (None, 19, 19, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 19, 19, 64)   36928       activation_213[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 19, 19, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_214 (Activation)     (None, 19, 19, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 19, 19, 256)  16640       activation_214[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 19, 19, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_71 (Add)                    (None, 19, 19, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_212[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_215 (Activation)     (None, 19, 19, 256)  0           add_71[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 19, 19, 64)   16448       activation_215[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 19, 19, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_216 (Activation)     (None, 19, 19, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 19, 19, 64)   36928       activation_216[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 19, 19, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_217 (Activation)     (None, 19, 19, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 19, 19, 256)  16640       activation_217[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 19, 19, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_72 (Add)                    (None, 19, 19, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_215[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_218 (Activation)     (None, 19, 19, 256)  0           add_72[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 10, 10, 128)  32896       activation_218[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 10, 10, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_219 (Activation)     (None, 10, 10, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 10, 10, 128)  147584      activation_219[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 10, 10, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_220 (Activation)     (None, 10, 10, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 10, 10, 512)  66048       activation_220[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 10, 10, 512)  131584      activation_218[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 10, 10, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 10, 10, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_73 (Add)                    (None, 10, 10, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_221 (Activation)     (None, 10, 10, 512)  0           add_73[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 10, 10, 128)  65664       activation_221[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 10, 10, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_222 (Activation)     (None, 10, 10, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 10, 10, 128)  147584      activation_222[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 10, 10, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_223 (Activation)     (None, 10, 10, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 10, 10, 512)  66048       activation_223[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 10, 10, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_74 (Add)                    (None, 10, 10, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_221[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_224 (Activation)     (None, 10, 10, 512)  0           add_74[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 10, 10, 128)  65664       activation_224[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 10, 10, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_225 (Activation)     (None, 10, 10, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 10, 10, 128)  147584      activation_225[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 10, 10, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_226 (Activation)     (None, 10, 10, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 10, 10, 512)  66048       activation_226[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 10, 10, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_75 (Add)                    (None, 10, 10, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_224[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_227 (Activation)     (None, 10, 10, 512)  0           add_75[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 10, 10, 128)  65664       activation_227[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 10, 10, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_228 (Activation)     (None, 10, 10, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 10, 10, 128)  147584      activation_228[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 10, 10, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_229 (Activation)     (None, 10, 10, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 10, 10, 512)  66048       activation_229[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 10, 10, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_76 (Add)                    (None, 10, 10, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_227[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_230 (Activation)     (None, 10, 10, 512)  0           add_76[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 5, 5, 256)    131328      activation_230[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_231 (Activation)     (None, 5, 5, 256)    0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_231[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_232 (Activation)     (None, 5, 5, 256)    0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_232[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 5, 5, 1024)   525312      activation_230[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 5, 5, 1024)   4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_77 (Add)                    (None, 5, 5, 1024)   0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_233 (Activation)     (None, 5, 5, 1024)   0           add_77[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_233[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_234 (Activation)     (None, 5, 5, 256)    0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_234[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_235 (Activation)     (None, 5, 5, 256)    0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_235[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_78 (Add)                    (None, 5, 5, 1024)   0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_233[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_236 (Activation)     (None, 5, 5, 1024)   0           add_78[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_236[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_237 (Activation)     (None, 5, 5, 256)    0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_237[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_238 (Activation)     (None, 5, 5, 256)    0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_238[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_79 (Add)                    (None, 5, 5, 1024)   0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_236[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_239 (Activation)     (None, 5, 5, 1024)   0           add_79[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_239[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_240 (Activation)     (None, 5, 5, 256)    0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_240[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_241 (Activation)     (None, 5, 5, 256)    0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_241[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_80 (Add)                    (None, 5, 5, 1024)   0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_239[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_242 (Activation)     (None, 5, 5, 1024)   0           add_80[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_242[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_243 (Activation)     (None, 5, 5, 256)    0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_243[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_244 (Activation)     (None, 5, 5, 256)    0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_244[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_81 (Add)                    (None, 5, 5, 1024)   0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_242[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_245 (Activation)     (None, 5, 5, 1024)   0           add_81[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 3, 3, 512)    524800      activation_245[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 3, 3, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_246 (Activation)     (None, 3, 3, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 3, 3, 512)    2359808     activation_246[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 3, 3, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_247 (Activation)     (None, 3, 3, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 3, 3, 2048)   1050624     activation_247[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 3, 3, 2048)   2099200     activation_245[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 3, 3, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 3, 3, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_82 (Add)                    (None, 3, 3, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_248 (Activation)     (None, 3, 3, 2048)   0           add_82[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 3, 3, 512)    1049088     activation_248[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 3, 3, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_249 (Activation)     (None, 3, 3, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 3, 3, 512)    2359808     activation_249[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 3, 3, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_250 (Activation)     (None, 3, 3, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 3, 3, 2048)   1050624     activation_250[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 3, 3, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_83 (Add)                    (None, 3, 3, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_248[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_251 (Activation)     (None, 3, 3, 2048)   0           add_83[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 3, 3, 512)    1049088     activation_251[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 3, 3, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_252 (Activation)     (None, 3, 3, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 3, 3, 512)    2359808     activation_252[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 3, 3, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_253 (Activation)     (None, 3, 3, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 3, 3, 2048)   1050624     activation_253[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 3, 3, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_84 (Add)                    (None, 3, 3, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_251[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_254 (Activation)     (None, 3, 3, 2048)   0           add_84[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_254[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "flatten_24 (Flatten)            (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "fc3 (Dense)                     (None, 3)            6147        flatten_24[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 22,472,067\n",
      "Trainable params: 22,422,019\n",
      "Non-trainable params: 50,048\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "resmodel.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "c6f5ae71",
   "metadata": {},
   "outputs": [],
   "source": [
    "resmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "e50f2250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "188/188 [==============================] - 1107s 6s/step - loss: 0.2636 - accuracy: 0.8952 - val_loss: 1.2496 - val_accuracy: 0.5487\n",
      "Epoch 2/10\n",
      "188/188 [==============================] - 1113s 6s/step - loss: 0.1642 - accuracy: 0.9363 - val_loss: 10.4451 - val_accuracy: 0.3367\n",
      "Epoch 3/10\n",
      "188/188 [==============================] - 1119s 6s/step - loss: 0.1335 - accuracy: 0.9472 - val_loss: 0.9215 - val_accuracy: 0.7420\n",
      "Epoch 4/10\n",
      "188/188 [==============================] - 1051s 6s/step - loss: 0.0941 - accuracy: 0.9647 - val_loss: 15.8455 - val_accuracy: 0.3460\n",
      "Epoch 5/10\n",
      "188/188 [==============================] - 865s 5s/step - loss: 0.0757 - accuracy: 0.9723 - val_loss: 1.8510 - val_accuracy: 0.7160\n",
      "Epoch 6/10\n",
      "188/188 [==============================] - 844s 4s/step - loss: 0.0621 - accuracy: 0.9775 - val_loss: 4.0214 - val_accuracy: 0.5607\n",
      "Epoch 7/10\n",
      "188/188 [==============================] - 845s 4s/step - loss: 0.0425 - accuracy: 0.9850 - val_loss: 2.9542 - val_accuracy: 0.6033\n",
      "Epoch 8/10\n",
      "188/188 [==============================] - 846s 5s/step - loss: 0.0449 - accuracy: 0.9858 - val_loss: 0.2367 - val_accuracy: 0.9307\n",
      "Epoch 9/10\n",
      "188/188 [==============================] - 848s 5s/step - loss: 0.0463 - accuracy: 0.9840 - val_loss: 0.9223 - val_accuracy: 0.8153\n",
      "Epoch 10/10\n",
      "188/188 [==============================] - 846s 5s/step - loss: 0.0351 - accuracy: 0.9878 - val_loss: 0.5644 - val_accuracy: 0.9173\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x273afe67f48>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resmodel.fit(X_train, Y_train, batch_size = 64, epochs = 10, validation_data=(X_val, Y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b7f5e3",
   "metadata": {},
   "source": [
    "Training accuracy is 98.8% - great news. But that also means that my Bayes error estimation is wrong - it's definietly more than 5%. On the other hand right now the variance is the problem - difference between training and validation accuracy is 7%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c86b8e0",
   "metadata": {},
   "source": [
    "# References and links"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319c62a9",
   "metadata": {},
   "source": [
    "[1] Lung and Colon Cancer Histopathological Image Dataset (LC25000)\n",
    "Andrew A. Borkowski, MD, Marilyn M. Bui, MD, PhD, L. Brannon Thomas, MD, PhD,\n",
    "Catherine P. Wilson, MT, Lauren A. DeLand, RN, Stephen M. Mastorides, MD\n",
    "\n",
    "\n",
    "[2] Tried to find histologic differences between two cancers, came across this: \n",
    "- https://www.pathologyoutlines.com/topic/lungtumoradenocarcinoma.html\n",
    "- https://www.pathologyoutlines.com/topic/lungtumorSCC.html\n",
    "\n",
    "[3] If you want to know more about patients with those two types of cancer and how it affect te survival:\n",
    "Differences Between Squamous Cell Carcinoma and Adenocarcinoma of the Lung: Are Adenocarcinoma and Squamous Cell Carcinoma Prognostically Equal? \n",
    "Akikazu Kawase, Junji Yoshida, Genichiro Ishii, Masayuki Nakao, Keiju Aokage, Tomoyuki Hishida, Mitsuyo Nishimura, Kanji Nagai\n",
    "https://academic.oup.com/jjco/article/42/3/189/1015061"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd14fa7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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