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.

Actively maintained and comprehensive public glaucoma dataset catalog

AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING

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.

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public full-fundus glaucoma images and associated metadata.

[npj Digital Medicine] "Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling" by Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H Van Tassel, Kyle Kovacs, Emily Y Chew, Zhiyong Lu, Zhangyang Wang, & Yifan Peng

Glaucoma detection using deep learning(cnn)

Health Economics simulation

Repositório com a parte prática do meu Trabalho de Conclusão de Curso III, referente ao algoritmo da arquitetura CapsNet para a classificação de imagens de retina em glaucomatosas e normais.

Design and implement an algorithm in Java to test visual loss of Glaucoma patients. The algorithm is designed to imitate the real tests which are expensive and done on a special hardwares. The program can run on a normal computer screen reducing the cost and increasing the availability of the eye test in rural areas.

This GUI is specifically designed to facilitate the analysis and processing of medical fundus images, with a particular focus on the detection and classification of defects in the retinal nerve fiber layer, which is crucial for the diagnosis and monitoring of glaucoma.

ACRIMA project

Code for the SIPAIM 2024 paper 'Automatic Glaucoma Classification and Justification Using a Large and Diverse Dataset'.

CASB 198 Capstone Research Project files

Convert hand written intraocular pressure graphs to csv.

IGFBPL1 Retinal Microglia Project | Dong Feng Chen Lab collaboration | Schepens Eye Research Institute, Mass General Hospital, Harvard Medical School

This project involves building a Glaucoma Detection AI-ML model using a Convolutional Neural Network (CNN) to classify retinal images as either "Glaucoma Affected" or "Normal." The model is trained using ImageDataGenerator for data augmentation, with binary cross-entropy loss, Adam optimizer, and is saved in `.keras` format.

Learning a thing or two about AI/ML in healthcare

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma