glaucoma

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

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

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.

[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)

Glaucoma detection app frontend (ML, medical)

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

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.

A deep learning framework for automated diagnosis of Diabetic Retinopathy, AMD, and Glaucoma using a Hybrid Attention-CNN model. Combines ResNet50 with self-attention for enhanced accuracy and interpretability. Includes Python implementation, preprocessing pipeline, training scripts, and case study documentation.

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

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.

Glaucoma detection from retinal fundus images — ResNet18 + U-Net CDR fusion · AUC 0.947 · FastAPI + React

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.

Reproducible optic disc/cup segmentation and public-to-clinical transfer study for AI-enhanced ophthalmoscopy.

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

a DINOv2 foundation model further pretrained on over 500k crops of the optic disc. See paper XXX for more information. If used, please cite accordingly.

ACRIMA project

AI-powered multi-disease eye disease detection system using EfficientNetB0, Grad-CAM Explainable AI, and Streamlit .