Official website of our paper: Applications of Deep Learning in Fundus Images: A Review. Newly-released datasets and recently-published papers will be updated regularly.

AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING

Joint Vessel Segmentation and Deformable Registration on Multi-Modal Retinal Images based on Style Transfer

Glaucoma detection automation project. Trained a binary image classifier using CNNs and deployed as a streamlit web app. It takes eye (retinal scan) image as input and outputs whether the person is affected by glaucoma or not.

Classification of Fundus Images into 5 stages of Diabetic Retinopathy, and segmentation of blood vessels in fundus images

Code for the paper "OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing"

Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images (IJCNN 2023)

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

Blood vessels and Exudates extraction for the detection of Diabetic Retinopathy

AI-powered diabetic retinopathy detection web app using EfficientNet with Grad-CAM visual explanations for interpretable medical diagnosis, developed for research and educational purposes.

Open-source glaucoma detection AI for mobile/low-resource clinics using synthetic training data

Diabetic Retinopathy using Patch Networks.

Binary classification of Diabetic Retinopathy using SVM in MATLAB with fundus image features.

Ultra: Multi-Granularity Topological Reasoning for Anatomically Consistent Vasculature Parsing

Research about glaucoma detection using CDR value. Trying to create a new and cheaper method of detecting glaucoma

Comparison of Classical and Deep Learning-Based Feature Representations for Age-Related Macular Degeneration

Multi-class classification of Diabetic Retinopathy using SVM and ANN in MATLAB.

Glaucoma detection from Deep Fundus Images using EfficientNet-B3 — binary classification of Glaucomatous Optic Neuropathy (GON+ vs GON-) on the HYGD dataset.

Comparing MobileNet and EfficientNet on Glaucoma Detection

Bilateral cross-attention fusion network for diabetic retinopathy urgency triage from fundus images. Classifies retinal scans into LOW / MEDIUM / HIGH clinical urgency using bilateral ResNet-50 with bidirectional spatial attention.