retinal-disease-detection

Code for the paper "nnMobileNet: Rethinking CNN for Retinopathy Research"

[TMI 2019] Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification

Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models

Deep learning pipeline for classification of Cataract, Diabetic Retinopathy, Glaucoma and Normal using fundus images

Retinal disease detection using optical coherence tomography (OCT) and deep neural networks.

Advanced Neural Network 2022 (XAI 612) Course Assignment

CNN-based heart disease prediction from retinal images — TensorFlow, Keras, Streamlit, transfer learning, 90%+ accuracy

This project leverages Convolutional Neural Networks (CNN) to detect Diabetic Retinopathy from retinal images. Diabetic Retinopathy is a severe eye disease that can cause vision loss or blindness in individuals with diabetes.

QD-RetNet: Efficient Retinal Disease Classification via Quantized Knowledge Distillation [MIUA-2025]

Hybrid Quantum–Classical ML model for multiclass retinal disease classification.

AI-powered retinal disease detection system using EfficientNetB3 transfer learning to classify fundus images into Diabetic Retinopathy, Glaucoma, Cataract, and Normal — built with TensorFlow, OpenCV, and Streamlit.

Retinal Optical Coherence Tomography Classification using Convolutional Neural Network (CNN)

Selective prediction with calibrated uncertainty for retinal disease screening — research code accompanying the paper.

Multi-model deep learning pipeline classifying retinal diseases (NORMAL, AMD, DR) on the OCTA-500 dataset. Implemented ResNet50/101 and AlexNet for projection maps and ViT-B/L/H for b-scan sequences, fused both modalities, and used 5-fold cross-validation and MAE pre-training.