This project aim to a build system which helps in the detection of cataract and it's type with the use of Machine Learning and OpenCv algorithms with the accuracy of 96 percent.

A deep learning model built to detect cataract in human eyes using the VGG-19 pretrained weights

Android app which uses Neural architecture to detect the type and grade of the cataract

Deep learning project for ocular eye disease classification

Enhancing cataract detection using a MEDNet-based model. Improved accuracy and speed with latent vectors and sampling techniques. Automated early detection for better patient outcomes and reduced ophthalmologist workload.

Cataract detection model

Cataract Detection System Using Deep Learning

Cataract classification from fundus images using a robust model that combines InceptionV3, VGG19, and InceptionResNetV2 through stacking, achieving an accuracy of 98.31%. This advanced approach ensures high precision and sensitivity, making it highly effective in distinguishing between cataract and normal cases.

Cataract classification

Our system works on the detection of cataracts and type of classification on the basis of severity namely; mild, normal, and severe, in an attempt to reduce errors of manual detection of cataracts in the early ages using Machine Learning and Transfer Learning

Design Project: A wearable device for disease detection, by processing image of the eye (Iridology).For the proof of concept and MVP, the software was able to differentiate between a healthy eye, and an eye with a cataract.

Cataract Diagnosis using AI and Neural Network

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.