1. Vision-Based Assistance for Vocal Fold Identification in Laryngoscopy with Knowledge Distillation.
- Author
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Thao Thi Phuong DAO, Minh-Khoi PHAM, Mai-Khiem TRAN, Chanh Cong Ha, Boi Ngoc VAN, Bich Anh TRAN, and Minh-Triet TRAN
- Abstract
Laryngoscopy images play a vital role in merging computer vision and otorhinolaryngology research. However, limited studies offer laryngeal datasets for comparative evaluation. Hence, this study introduces a novel dataset focusing on vocal fold images. Additionally, we propose a lightweight network utilizing knowledge distillation, with our student model achieving around 98.4% accuracy-comparable to the original EfficientNetB1 while reducing model weights by up to 88%. We also present an AI-assisted smartphone solution, enabling a portable and intelligent laryngoscopy system that aids laryngoscopists in efficiently targeting vocal fold areas for observation and diagnosis. To sum up, our contribution includes a laryngeal image dataset and a compressed version of the efficient model, suitable for handheld laryngoscopy devices. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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