Eye Disease Detection using UNet Model for Image Segmentation with Optic Disc and Cup Segmentation Methods and Deep Learning Algorithm
The Ocular Disease Detection project is an AI-powered web application designed to detect common ocular diseases from digital images. Built with PyTorch and Streamlit, the application uses a custom-trained Convolutional Neural Network (CNN) to classify images into six distinct categories: AMD, Cataract, Glaucoma, Myopia, Normal and non eye images
Modular Vision-Based Multi-task Learning for Eye Disease Diagnosis
This system will revolutionize digital healthcare by merging machine learning–based disease classification, explainable AI screening, remote consultations, home doctor services, and emergency map support.
AI-Powered Eye Disease Detection Web App An intelligent retina image classification system built using deep learning (VGG16), TensorFlow, and Flask. This open-source project helps detect common eye diseases like Cataract, Diabetic Retinopathy, and Glaucoma, and also identifies uncertain cases as Unknown.
AI-powered Strabismus Screening Application
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