This repository houses machine learning models and pipelines for predicting various diseases, coupled with an integration with a Large Language Model for Diet and Food Recommendation. Each disease prediction task has its dedicated directory structure to maintain organization and modularity.

The project uses natural language processing and information retrieval to create an interactive system for user queries on a collection of PDFs. It involves loading, segmenting, and embedding PDFs with a Hugging Face model, utilizing Pinecone for efficient similarity searches

Project COLON-X (Shaping the neXt frontier in intelligent COLONoscopy)

AI Toolkit for Healthcare Imaging in Keras 3

Code for IJCV-From Small to Large: In-Context Learning as a New Paradigm for Domain Generalization

🌐 A full-stack telehealth platform enabling virtual consultations, real-time symptom prediction via AI, and chatbot-assisted triage — built with React, Node.js, and Python.

video capsule endoscopy abnormality classification for capsule vision 2024 challenge

Real-ESRGAN model deployed on Android using NCNN (C++/JNI) and ExecuTorch (Java) for real-time image super-resolution in dermatology, with reproducible benchmarking (PSNR, SSIM, MSE, LPIPS).

Health Information Website with Chatbot Source Code

Official PyTorch Implementation of paper 'Edge-Based Graph Neural Networks for Cell-Graph Modeling and Prediction'

A TSX-based wellness and cycle care tracking app with mood journal, symptom tracker, and AI assistant.

A curated list of tools, frameworks, applications, datasets, and resources for AI in Healthcare.

💻🔒 A local-first full-stack app to analyze medical PDFs with an AI model (Apollo2-2B), ensuring privacy & patient-friendly insights — no external APIs or cloud involved.

This project uses OCR and machine learning to extract CBC values from reports and predict urgency levels. As of now, it supports image/pdf inputs, manual corrections, and SHAP explainability. Ideal for medical AI, healthcare OCR, and automated lab report analysis.

A deep learning framework for automated diagnosis of Diabetic Retinopathy, AMD, and Glaucoma using a Hybrid Attention-CNN model. Combines ResNet50 with self-attention for enhanced accuracy and interpretability. Includes Python implementation, preprocessing pipeline, training scripts, and case study documentation.

💻🔒 A local-first full-stack app to analyze medical PDFs with an AI model (Apollo2-2B), ensuring privacy & patient-friendly insights — no external APIs or cloud involved.

This system will revolutionize digital healthcare by merging machine learning–based disease classification, explainable AI screening, remote consultations, home doctor services, and emergency map support.

Binary classification of breast cancer using PyTorch. Used StandardScaler, LabelEncoder, Dataset, DataLoader, custom nn.Module model, BCELoss, and SGD. Focused on implementing a complete training pipeline, not optimizing accuracy.

Smart solution addressing PTSD, security, and post-service instability for military personnel

ChannelMAE is a combination of MAE and ChannelViT