CSE'26 | AI, ML & Data Science Enthusiast | Building real-world projects and solving problems with code

SANGAM ACHARYA  • Peerlist

<ul><li>AI-powered breast cancer detection from histopathology images using deep learning</li><li>Deep learning system for accurate breast cancer classification from tissue images</li><li>Automated histopathology image analysis for breast cancer diagnosis</li><li>CNN-based medical imaging solution for malignant and benign tissue classification</li><li>Computer vision for breast cancer detection with 91% classification accuracy</li><li>AI-assisted breast cancer screening through histopathology image analysis</li><li>Medical image intelligence platform for breast cancer diagnosis</li><li>Custom CNN model outperforming transfer learning baselines in cancer classificationTurning Histopathology Images into Life-Saving Insights</li></ul>

Breast cancer detection

Developed a Retrieval-Augmented Generation (RAG) based Document Question Answering system that enables users to upload PDF documents and interact with them through natural language queries. The application processes documents, generates semantic embeddings, stores them in a vector database, retrieves the most relevant context using similarity search, and leverages Large Language Models (LLMs) to generate accurate, context-aware responses. Built with Streamlit for an interactive user experience, the system reduces hallucinations by grounding answers in the uploaded document content, providing reliable and efficient document intelligence for knowledge retrieval and exploration.