You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This project uses YOLO for real-time leukemia detection in blood samples and CNNs for classifying brain hemorrhages in MRI scans. It aims to support faster, more accurate medical diagnostics through deep learning.
This project primarily focuses on addressing the issue of early detection of learning disabilities in students, with a specific focus on dyslexia and attention deficit hyperactivity disorder (ADHD).
This repository houses a workflow that uses biological feature trees to segregate cancer RNA-seq datasets, then it trains machine learning models to predict the presence or absence of known, cancer-associated DNA-level mutations.
Built an end-to-end deep learning pipeline using ResNet-50 to classify retinal images into five stages of Diabetic Retinopathy. Applied transfer learning, image preprocessing, and AUC-based evaluation on the APTOS 2019 Kaggle dataset, achieving a 94% validation AUC—offering real-world potential in clinical diagnosis automation.
📊 Multiple Disease Prediction System 🏥 An intelligent healthcare system for predicting and diagnosing multiple diseases using machine learning and data analysis. Empowering early detection and better patient care. Disease Prediction: Predict the likelihood of various diseases, including heart diseases, diabetes, and more.