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arXiv · 2411.05876

Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review

Mahtab Faraji·Homa Rashidisabet·George R. Nahass·RV Paul Chan·Thasarat S Vajaranant·Darvin Yi
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Abstract

Here, we examine the latest advances in glaucoma detection through Deep Learning (DL) algorithms using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study focuses on three aspects of DL-based glaucoma detection frameworks: input data modalities, processing strategies, and model architectures and applications. Moreover, we analyze trends in employing each aspect since the onset of DL in this field. Finally, we address current challenges and suggest future research directions.

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Mahtab Faraji, Homa Rashidisabet, George R. Nahass, RV Paul Chan, Thasarat S Vajaranant, Darvin Yi. 2024-11-07. Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review. https://arxiv.org/abs/2411.05876

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