Deep self-organizing competitive neural network for magnetically controlled capsule endoscopy-based auxiliary diagnosis of clinically significant gastric lesions

doi:10.12998/wjcc.126704

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World Journal of Clinical Cases

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2307-8960

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Clin Cases. Sep 26, 2026; 14(27): 126704
Published online Sep 26, 2026. doi: 10.12998/wjcc.126704

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Figure 1 The process of study: Data collection primarily considers the richness of the data. Dataset construction can achieve data standardization. Neural networks include text vectorization, deep feature mining, and clustering. Data analysis examines the correlation and clustering effects. IM: Intestinal metaplasia.

Citation: Song YY, Wang CM, Chen ZY, Xu PJ, Zhang J. Deep self-organizing competitive neural network for magnetically controlled capsule endoscopy-based auxiliary diagnosis of clinically significant gastric lesions. World J Clin Cases 2026; 14(27): 126704
URL: https://www.wjgnet.com/2307-8960/full/v14/i27/126704.htm
DOI: https://dx.doi.org/10.12998/wjcc.126704