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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
🌐 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.
💻🔒 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.
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.
🏥 DICOM Flask App – AI-Powered Lesion Detection A Flask-based web app for uploading, processing, and analyzing DICOM medical images. Uses DeepLesion (Faster R-CNN) for lesion detection and ResNet50 for classification. Features a multi-tab UI with sidebar navigation. A sample DICOM file is included for testing!
AI-powered Flask app that extracts structured medical data from prescription images using Amazon Bedrock. Upload, scan, and get clean results with zero hassle — perfect for health tech, clinics, or personal use. 💊📄
An AI-powered gamified rehabilitation and wellness platform helping users recover from mental health, addiction, and diet-related challenges through personalized tasks, therapy tracking, and predictive analytics.