AI Cataract – Multi-Modal Deep Learning System for Cataract Detection and Accurate Classification

Cataracts are a major cause of preventable blindness, requiring early detection and accurate staging to enable timely treatment and reduce visual impairment. Traditional diagnostic methods depend on clinical examination and imaging interpretation, which may be limited in resource-constrained settings. Existing deep learning-based systems primarily perform binary classification between normal and cataract conditions, lacking the ability to distinguish disease stages and provide detailed clinical insights.

This project introduces the Multi-Architecture Ocular Classification System (MAOCS), developed as a full-stack web application using Python and the Django framework in accordance with IEEE academic standards. The framework integrates multiple deep learning architectures, including Convolutional Neural Networks, VGG16, and MobileNet, to enhance classification accuracy through comparative model evaluation.

MAOCS performs multi-class classification of ocular images into normal, immature cataract, and mature cataract stages. The system employs standardized preprocessing techniques such as normalization, resizing, and data augmentation to improve robustness across varying imaging conditions.

The web-based platform enables users to upload fundus images for real-time classification, while administrative dashboards support model comparison, dataset management, and performance monitoring. Designed strictly for academic and research purposes, this project demonstrates applied medical image analysis, multi-model evaluation, and full-stack web deployment for advanced cataract detection and staging systems.

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ieeeprojects, pythonprojects, djangoprojects, pythonwebapplications, pythonfullstackprojects, computerscienceprojects, computersciencefinalyearprojects, cseprojects, itprojects, finalyearprojects, finalsemprojects, finalyearstudentsprojects, btechprojects, beprojects, mtechprojects, meprojects, mcaprojects, mscprojects, majorprojects, miniprojects, liveprojects, researchorientedprojects

CATEGORY:
Education

AUDIENCE:
B.E, B.Tech, MCA, MSc, M.E, M.Tech, BCA and BSc – Universities in India & Abroad

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DISCLAIMER:
This project is developed strictly for academic and research purposes following IEEE guidelines.

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