Platform Architecture & Overview
OculusAI is a personal academic and vision science demonstration project created by Aditya to explore deep transfer learning and interactive optometric screening.
Core Subsystems
MobileNetV2 inverted residual network running 256x256 image tensors to detect Diabetic Retinopathy, Glaucoma, Cataract, and Normal tissue.
- • Transfer-learned feature extractor
- • Circular fundus edge validator
- • Multi-class probability distribution
Custom 8-layer deep convolutional neural network processing 128x128 plate crops to recognize embedded numerical digits (0-9).
- • 4-block Conv2D + BatchNorm + Dropout
- • 99.52% top-1 validation accuracy
- • Deutan vs Protan likelihood estimation
Physiological color matrix transformation pipeline calculating real-time spectral absorption variations across all cone classes.
- • Protanopia, Deuteranopia, Tritanopia
- • Cataract optical turbidity filter
- • Dual-view interactive split slider
Angular resolution testing utilizing international standard ISO 8596 Tumbling E optotypes with physical display calibration.
- • Standard 5 arcminute resolution
- • Snellen 20/200 down to 20/15
- • Directional response logging
Technology Stack
Developed as a personal portfolio project. Models generate experimental probability distributions and vision emulations intended for portfolio presentation and academic study rather than certified medical usage.