Brain Tumor Classification with PyTorch (CNN)

This project implements a Convolutional Neural Network in PyTorch to classify brain tumor types from MRI scans.

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🧠 Project Overview

The goal of this project is to build and train a CNN capable of classifying MRI brain images into four tumor categories. The model uses PyTorch’s deep learning framework, trained on a Kaggle brain tumor MRI dataset, and achieves strong accuracy on unseen test data.

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📂 Features

• End-to-end PyTorch workflow
• Image preprocessing using torchvision.transforms
• Custom CNN architecture with Convolution, ReLU, MaxPooling, Flatten, and Linear layers
• Training loop with forward/backward pass + parameter optimization
• Model evaluation with accuracy calculation
• Visualization of predictions on random images
• Achieves ~96% test accuracy

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🛠️ Technologies Used

• Python
• PyTorch
• Torchvision
• Matplotlib
• Jupyter Lab / Notebook

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📦 Requirements

You will need:

• Python 3.x
• PyTorch + Torchvision
• Matplotlib
• Jupyter Lab / Notebook

Install everything with:

pip install torch torchvision matplotlib jupyterlab

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🤝 Acknowledgments

This project is based on the concepts taught in the YouTube tutorial. All dataset credit belongs to the original Kaggle contributors.