Enhancing Vocal-Based Laryngeal Cancer Screening with Additional Patient Information and Voice Signal Embedding
- Authors
- Song, Jaemin; Lee, Yong Oh; Park, Seho; Lee, Youn Kyu; Park, Hansang; Kim, Hyun-Bum
- Issue Date
- 2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Deep learning; Laryngeal cancer; Patient information; Vocal-based screening; Voice signal analysis
- Citation
- Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, pp 3731 - 3735
- Pages
- 5
- Journal Title
- Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
- Start Page
- 3731
- End Page
- 3735
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32742
- DOI
- 10.1109/BigData59044.2023.10386634
- ISSN
- 0000-0000
- Abstract
- Symptoms of laryngeal cancer manifest primarily through voice changes, and its diagnosis relies solely on laryngoscopy examinations, lacking objective indicators of voice alterations. Recent advances in deep learning have opened possibilities for vocal-based laryngeal cancer screening. However, the practical medical application remains constrained due to relatively low accuracy. In this paper, we propose a method that combines patient information and voice analysis with a CNN model to address this issue. Experiments demonstrate a 8% improvement in accuracy when additional information is embedded alongside voice signals, compared to using voice data alone in deep learning models. This approach holds promise for more effective laryngeal cancer screening and diagnosis. © 2023 IEEE.
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Collections - College of Engineering > Computer Engineering > Journal Articles
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