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VOOZH | about |
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.
Android app which uses Neural architecture to detect the type and grade of the cataract
Deep learning pipeline for classification of Cataract, Diabetic Retinopathy, Glaucoma and Normal using fundus images
This GitHub repo contains a Cataract-Prediction project deployed using Flask web framework. The project involves the development of a machine learning model to predict the presence of cataracts in eye images. The Flask web application allows users to upload eye images and receive a prediction of cataract presence.
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.
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