Eye Disease Detection using UNet Model for Image Segmentation with Optic Disc and Cup Segmentation Methods and Deep Learning Algorithm
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.
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
Benchmarking CNN Architectures for Multi-Class Retinal Disease Classification
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-based screening tool for early detection of eye diseases using retinal fundus images.
AI-powered Strabismus Screening Application
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