medical-diagnostics

PyTorch implementation of Grouped SSD (GSSD) and GSSD++ for focal liver lesion detection from multi-phase CT images (MICCAI 2018, IEEE TETCI 2021)

MediScan: AI-powered bone fracture detection system achieving 99.8% accuracy through deep learning. Features real-time X-ray analysis, transparent Grad-CAM visualizations, and clinical integration tools. Built with Python/FastAPI backend and responsive HTML/CSS frontend, making advanced medical diagnostics more accessible to healthcare providers.

Sparrow AI - API for disease diagnostics

Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.

Medical Diagnostic Module Programmed with Python and using Fuzzy Logic

Medical Diagnosis system using Prolog

A medical diagnosis system

Machine learning models for detection of diseases.

CoughLens AI is a multilingual, voice-driven diagnostic assistant that analyzes cough audio

Egészségügyi szűrőprogramok, orvosi tesztek eredményességének vizsgálata.

Quick Android app for medical diagnosis, currently just using Endless Medical API

CancerGuardian is a machine learning-powered web app that helps predict breast cancer diagnoses based on cytology measurements. 🩺✨ Built with Streamlit, Scikit-Learn, and Plotly, this tool visualizes tumor characteristics and provides predictions using a trained model. 🚀

Machine learning model in Julia classifies patients with Diabetes Mellitus, Diabetic Retinopathy, and Diabetic Nephropathy using biomarkers Hornerin and SFN, plus clinical features like age and diabetic duration, for diagnostic applications.

A web application that predicts heart disease risk using a tuned Random Forest machine learning model. Built with a decoupled architecture , it utilizes a custom 35% safety threshold to prioritize patient safety (recall) and minimize missed detections

Advanced machine learning system for diabetes risk prediction using clinical biomarkers and patient health data

Breast cancer is one of the most common cancers in women worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved survival rates. This project utilizes machine learning algorithms to classify breast cancer cases as either malignant or benign based on medical diagnostic features.

AI-driven disease prediction model based on symptoms using machine learning.

This project uses data mining and ML to enhance liver disease detection. Analyzing clinical markers like bilirubin and enzymes from the ILPD dataset , I compared six models. Random Forest proved most accurate at 72.57% , offering a non-invasive tool for early diagnosis and better patient care.

This repository features cutting-edge machine learning applications in healthcare, addressing diverse challenges such as dermatological lesion detection, ECG signal categorization, gland segmentation in colorectal cancer, pathological myopia prediction, and pneumothorax identification.

A deep learning model built with TensorFlow for the rapid, non-invasive classification of Type 2 Diabetes using Raman spectroscopy data. Achieves 93.75% accuracy with 100% recall for diabetic cases.