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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.
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
Cataract detection model
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.
Lightweight TinyVGG Inspired CNN 1.35M Parameter with Squeeze-and-Excitation blocks for ocular disease classification
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
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.
AI-powered mobile app for early-stage cataract detection. Analyze eye images instantly with on-device processing - works offline, no internet required.
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.
A quantum-powered web application for detecting cataracts in eye images using a hybrid quantum-classical machine learning model.
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 Diagnosis using AI and Neural Network
AI-driven diagnostic tool for Cataract detection using Deep Learning. This project implements Convolutional Neural Networks (CNN) to analyze ocular images, providing automated screening to assist in early-stage diagnosis and medical intervention.
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