PredictiX is a comprehensive multi-disease prediction system built using the MERN stack and integrated with machine learning models. It accurately predicts lung cancer, breast cancer, diabetes, and heart disease, providing a seamless user experience for health diagnostics.

A Deep Learning solver for the Shallow Water Equations

PredictiX is a comprehensive multi-disease prediction system built using the MERN stack and integrated with machine learning models. It accurately predicts lung cancer, breast cancer, diabetes, and heart disease, providing a seamless user experience for health diagnostics.

Early Detection of Diabetic Retinopathy System, an application, uses machine learning to assess diabetic retinopathy risk. Input your health data and get results within seconds: Ranging from ['Mild', 'Moderate', 'Severe', 'No_DR']

This repository contains a deep learning project for detecting car number plates using object detection techniques and the InceptionResNetV2 model. The project is built on a dataset of car images with annotated bounding boxes around the number plates.

An image classification app built using Django 4, Django REST Framework 3, Next.js 12, and Material UI 5. The app uses Inception-ResNet-v2 to classify images selected by the user.

This repository hosts the Cervical Cancer Image Classification project, a comprehensive effort aimed at improving the classification accuracy of Squamous Cell Carcinoma (SCC) through advanced deep learning models and ensemble techniques. The project utilizes the Herlev dataset.

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

This project leverages the power of deep learning to differentiate between real and fake face images. An Inception ResNet V1 model was trained on a dataset of 140,000 images, equally divided between real and fake faces, to achieve high accuracy in identifying fraudulent attempts at digital impersonation.

Swin Transformer + Inception-ResNet = Improved Performance ✨ Evaluated on a Retinal OCT dataset.

Address the crowd counting problem on the Mall dataset (sparse) by exploring regression-based (Xception) and density-based (CSRNet) approaches.

benchmark of object detection algorithms for license plate detection

Gathering and labeling data for an image-text fusion model for flavor classification of food based on recipes. Generating images using stable diffusion models, and using deep classification models like BERT, BiLSTM, and InceptionResnet.

Explore diverse computer vision projects using Transfer Learning(TL), Convolutional Neural Networks (CNN), Autoencoder and more in this collaborative repository

End-to-end Image Classification using Deep Learning toolkit for custom image datasets. Features include Pre-Processing, Training with Multiple CNN Architectures and Statistical Inference Tools. Special utilities for RAM optimization, Learning Rate Scheduling, and Detailed Code Comments are included.

Explore my comprehensive collection of AI models for blood cancer detection. Leveraging deep learning and medical imaging, these models aim to revolutionize early diagnosis and treatment, making a significant impact on the battle against blood cancers. #AI #HealthcareInnovation

A from-scratch SOTA PyTorch implementation of the Inception-ResNet-V2 model designed by Szegedy et. al., adapted for Face Emotion Recognition (FER), with custom dataset support.

Classify the severity stages of Diabetic Retinopathy

This project is a web application that uses YOLOv5 and InceptionResNetV2 models for license plate detection and Optical Character Recognition (OCR) text extraction. The web applications were built using streamlit and flask