AI-Powered Eye Disease Detection & Colour Vision Testing
OculusAI is an academic demonstration project showcasing the application of deep learning technology in medical imaging, specifically for detecting eye diseases from retinal fundus images and assessing colour vision deficiencies.
This project demonstrates how convolutional neural networks can be trained to assist in identifying common eye conditions and colour blindness patterns, providing a comprehensive proof-of-concept for AI in ophthalmology and vision science.
How It Works
Eye Disease Detection
Upload a retinal fundus image (JPEG, PNG, or GIF format)
Image is preprocessed and resized to 256×256 pixels
CNN model analyzes the image and predicts disease class
Get detailed results with confidence scores for all 4 classes
Colour Blindness Test
Take an interactive Ishihara-style colour vision test
AI model predicts the correct digit in each colour plate (99.5% accuracy)
System analyzes error patterns across 4 colour type variations
Receive probability-based diagnosis for Protan/Deutan deficiencies
Technology Stack
Dual AI Models
Disease detection + Ishihara digit recognition (99.5% accuracy)
Modern Frontend
Next.js 16 with React & TypeScript
Fast Backend
Flask API with multi-model inference
Advanced Analysis
Probability-based colour blindness diagnosis
Detection Capabilities
Eye Diseases (Retinal Analysis)
Cataract - Lens clouding
Diabetic Retinopathy - Retinal blood vessel damage
Glaucoma - Optic nerve damage
Normal - Healthy eye
Colour Vision Deficiencies (Ishihara Test)
Deuteranomaly - Reduced green sensitivity (mild)
Deuteranopia - Complete green colour blindness (moderate to strong)
Protanomaly - Reduced red sensitivity (mild)
Protanopia - Complete red colour blindness (moderate to strong)
Project Purpose
This is an academic project created to demonstrate the potential of AI in medical imaging. It serves as a proof-of-concept for how deep learning can be applied to assist healthcare professionals in early disease detection.
The project showcases modern web technologies, machine learning workflows, and user-centered design principles for medical applications.
Important Notice
This application is a demonstration project for educational and research purposes only. It is not intended for clinical use or as a substitute for professional medical diagnosis. Always consult qualified ophthalmologists for actual eye health concerns and medical advice.