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

Autor:	Faraji, Mahtab, Rashidisabet, Homa, Nahass, George R., Chan, RV Paul, Vajaranant, Thasarat S, Yi, Darvin
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.05876 Zobrazit plný text záznamu View this record from Arxiv