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"

Blood vessels and Exudates extraction for the detection of Diabetic Retinopathy

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

Diabetic Retinopathy using Patch Networks.

Comparing MobileNet and EfficientNet on Glaucoma Detection

The aim of project is detecting the type of disease eye suffers from by using fundus images. The majority of the identification models in use exclusively concentrate on one particular ocular disease. Therefore, My goal was to create a model for automatically classifying many ocular diseases using fundus photos as input and reporting disease type.