Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance

We developed a novel CAD system based on a deep neural network for polyp detection, and the Yamada classification outperformed that of nonexpert endoscopists. This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.

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Publication Details

Journal
World Journal of Gastroenterology
Published
2025-10-27
DOI
https://doi.org/10.3748/wjg.v31.i40.111120
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
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article

Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance

Guiming Liu, X. Y. Zhang, Quansheng Guan, Weiguang Qiao et al.
World Journal of Gastroenterology
Machine Learning and Data Classification
article

Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance

Guiming Liu, X. Y. Zhang, Quansheng Guan, Weiguang Qiao, Wenqing Wu, Jian–Jiun Ding, Si-Mei Chen, Lin Qiu, Chyong‐Huey Lai, Shunhui He, Yang Bai, Weiming Lü, Jie Dai, Licai Deng, Lingling Wang, Zhenyu Wang, Li Feng, Zhijian Li, Zhifang Zhao, Rui-Ya Zhang, Lizhi Yi, Hui Yang, Biaokai Zhu, Li-Min Cheng, Wenqian Shen, Wei-Jian Lun
article en

Abstract

We developed a novel CAD system based on a deep neural network for polyp detection, and the Yamada classification outperformed that of nonexpert endoscopists. This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.

World Journal of GastroenterologyVol. 31(40)
Nanfang Hospital (CN), Southern Medical University (CN)
Openalex Percentile: Top 84%
Machine Learning and Data Classification
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