MedEvalKit: A Unified Medical Evaluation Framework

An implementation of the paper Brain2Qwerty that translates brain EEG data into text for reading people's brains. There was no code so we made an implementation

MICCAI 2024: Welcome to the official repository of D-MASTER: Mask Annealed Transformer for Unsupervised Domain Adaptation in Breast Cancer Detection from Mammograms. This repository hosts the source code, pre-trained model weights, and benchmark dataset RSNA-BSD1K, supporting research in cross-domain breast cancer detection.

This deep learning model detects eye diseases using CNNs. Trained on an image dataset, it predicts conditions with high accuracy. Ideal for AI-driven medical diagnosis!

🔬 Aplikasi deteksi penyakit kulit menggunakan AI dan Computer Vision - Lomba Digiwar #1: Digital Application Challenge - VISIONARY: Unleashing Innovation through Computer Vision.

Deep learning model using VGG16 to classify chest X-rays into COVID-19, Pneumonia, TB, and Normal. Deployed via a local web app with real-time predictions.

Skin Diseases Detection System

This project applies machine learning to predict heart disease using clinical data. It covers data preprocessing, model building, and performance evaluation, aiming to support early diagnosis and healthcare decision-making through data-driven insights and AI-based prediction techniques.

A deep learning model for classifying eye diseases using Convolutional Neural Networks (CNNs). Built with TensorFlow and Keras, trained on a medical eye dataset for accurate multi-class classification and analysis.

PyTorch-based pipeline includes data preprocessing, model inference, and performance evaluation with standard metrics (Dice score, Hausdorff distance). The repository provides tools for visualizing segmentation results and comparing MedSAM-2's performance against baseline models, offering insights into adapting foundation models for medical imaging

Deep learning model for brain cancer detection using CNN architecture. Trained on multi-class MRI data with ImageDataGenerator and optimized with Adam

SEHAT is an interactive Predictive Health Application using Streamlit and machine learning models to forecast diabetes and heart disease risks based on user health parameters. Enhance your health awareness with immediate insights and proactive management tools.

Deep learning model for brain tumor classification using MRI images. Built with TensorFlow, Keras, and OpenCV, trained on CNN architecture, and optimized with Adam. 🎯🔥

This repository presents an efficient approach for fine-tuning large language models for the medical domain using 4-bit quantization and LoRA techniques.

Lung Cancer Classification app with FastAPI and Docker

🩺 Complete Health Diagnostic Hub – A 🌐 web-based platform using 🤖 machine learning to predict potential health risks for ❤️ heart, 🩸 kidney, 🏥 liver, and 🩹 diabetes conditions.

Predict COVID-19 from chest X-rays using a VGG16-based deep learning model. Fast, accurate, and deployed locally with a simple web interface

🩺 Complete Health Diagnostic Hub – A 🌐 web-based platform using 🤖 machine learning to predict potential health risks for ❤️ heart, 🩸 kidney, 🏥 liver, and 🩹 diabetes conditions.