Qingnang Smart Diagnosis is an end-to-end AI healthcare framework with field-proven application capabilities, designed to provide efficient and intelligent solutions for the medical industry.

AI-powered chest X-ray pneumonia detection with 86% accuracy and 96.4% sensitivity, validated on an independent (cross-operator) cohort of 485 pediatric samples. Built with TensorFlow & FastAPI.

ARI2201 - IAPT · Comparative analysis of machine learning and deep learning models for automated classification of lung respiratory sounds using audio feature extraction to support pulmonary disease detection.

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.

🩺🤖 PulmoVision Pro — AI-powered Pneumonia Detection from Chest X-Rays with Grad-CAM interpretability & Streamlit dashboard

EEG artifact removal pipeline using ICA on the CHB-MIT dataset with automated quality evaluation.

This GitHub repository hosts the notebooks and tools developed as part of this thesis to automate the extraction, processing, and analysis of data from the MICCAI 2023 conference, aiding in the systematic review and providing a structured foundation for further research in this crucial area.

Research-oriented ML pipeline for Hypertrophic Cardiomyopathy (HCM) outcome prediction using Clinical data & Genetic Variants (SNPs). Features XGBoost/LightGBM models, Isotonic Calibration, and Threshold Optimization

AI Lung Cancer Detection 🫁 | 95.7% Accuracy 📈 | VGG16 Transfer Learning 🧠. High-sensitivity classification (101/102 Malignant) using IQ-OTH/NCCD dataset. Optimized for clinical reliability via data sanitization and TensorFlow/Keras 🛠️. Focused on life-saving precision & scientific integrity ✨.

MediScan Mentor is an innovative application designed to assist medical students in interpreting CT scans.

Machine‑learning project for early detection of heart disease using clinical parameters (age, cholesterol, blood pressure, etc.). Implements multiple classifiers and provides evaluation metrics — ideal for health‑data research and risk prediction.

NeuroSense is an AI-powered clinical decision support web application for early risk assessment of Alzheimer’s Disease, Parkinson’s Disease, and Dementia using multimodal clinical data, biomarkers, and machine learning models.

An AI-powered web application to analyze medical images such as X-rays, MRIs, CT scans, and ECGs, helping doctors and technicians make faster and more accurate preliminary diagnoses.

An end-to-end system for automated Diabetic Foot Ulcer (DFU) grade classification, treatment recommendation, explainability, and report generation — integrated with a mobile app and powered by deep learning and Supabase.

Beat chronic illness through nutrition.

An intelligent machine learning model for classifying breast cancer cells as benign or malignant using the UCI Breast Cancer Wisconsin dataset.

This project involves conducting an exploratory data analysis on the Cleveland Heart Disease dataset and developing numerous machine learning models to predict the presence of heart disease.

Official Documentation And Concept Notes For The WLT AI Startup Within The Field Of Medicine And Biology.

OghuzHealthAI Is A Healthcare-Focused Artificial Intelligence Initiative That Applies Machine Learning And Deep Learning Techniques To Clinical Data Analysis, Medical Prediction, And Decision Support Systems. The Project Aims To Bridge Modern AI Technologies With Real-World Medical Applications.