Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2

Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2

AIM: To evaluate the application of an intelligent diagnostic model for pterygium. METHODS: For intelligent diagnosis of pterygium, the attention mechanisms—SENet, ECANet, CBAM, and Self-Attention—were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model. Th...

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Bibliographic Details
Main Authors: Mao-Nian Wu, Kai He, Yi-Bei Yu, Bo Zheng, Shao-Jun Zhu, Xiang-Qian Hong, Wen-Qun Xi, Zhe Zhang
Format: Article
Language:English
Published: Press of International Journal of Ophthalmology (IJO PRESS) 2024-07-01
Series:International Journal of Ophthalmology
Subjects:
deep learning
attention mechanism
pterygium
intelligent diagnosis
Online Access:http://ies.ijo.cn/en_publish/2024/7/20240702.pdf

Internet

http://ies.ijo.cn/en_publish/2024/7/20240702.pdf

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