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

• URL: http://arxiv.org/abs/2411.05876v1
• Date: Thu, 07 Nov 2024 23:35:05 GMT
• Title: Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review
• Authors: Mahtab Faraji, Homa Rashidisabet, George R. Nahass, RV Paul Chan, Thasarat S Vajaranant, Darvin Yi,
• Abstract summary: 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.
• Score: 0.2940464448991482
• License:
• 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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