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VOOZH | about |
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)
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.
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.
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.
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.
Open-source glaucoma detection AI for mobile/low-resource clinics using synthetic training data
ACRIMA project
Code for the SIPAIM 2024 paper 'Automatic Glaucoma Classification and Justification Using a Large and Diverse Dataset'.
🩺 Enhance glaucoma detection with GF-Care, a deep learning tool for automatic optic disc and cup segmentation from fundus images for accurate CDR calculation.
IGFBPL1 Retinal Microglia Project | Dong Feng Chen Lab collaboration | Schepens Eye Research Institute, Mass General Hospital, Harvard Medical School
CASB 198 Capstone Research Project files
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