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Affordable Deep Learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography
Ophthalmic Medical Image Analysis — OMIA 2024 workshop @ MICCAI, Lecture Notes in Computer Science, 2024
Abstract
Retinal diseases are a significant global health concern, often leading to severe vision impairment or blindness if not diagnosed and treated promptly. Classifying inherited retinal diseases (IRDs) is particularly challenging, typically requiring genetic analysis and expert ophthalmologists. Developing automatic deep learning models for their classification is crucial, especially for regions with low-resource settings. This work focuses on diagnosing IRDs and other common retinal conditions using color fundus photographs (CFPs), leveraging transformative advances in deep learning. Our approach utilizes a ResNet18 model trained on CFPs from seven diverse, multi-institute sources, aiming to classify 21 retinal diseases, including 13 IRDs. This is the highest number of IRD classes addressed by a single model using color fundus photographs to date. The model achieved an impressive score of 0.86 for Retinitis …
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Cite (BibTeX)
@inproceedings{farjallah2024affordable,
author = {Farjallah, Elyes and Shamieh, Said El and Rezaei, Razieh and Herrmann, Philipp and Künzel, Sandrine H and Holz, Frank G and Albarqouni, Shadi},
booktitle = {International Workshop on Ophthalmic Medical Image Analysis},
organization = {Springer Nature Switzerland Cham},
pages = {83--93},
title = {Affordable Deep Learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography},
year = {2024}
}