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

Table 2 Accuracy analysis in different dataset

		Conventional			DSomNN
Type	Number	Normal	Need	Rate, %	Normal	Need	Rate, %
a∗	n = 500	402	98	19.60	410	90	18.00
b∗	n = 500	399	101	20.20	401	99	19.80

The data of a∗ samples from intubation gastroscope, and the data of b∗ samples from magnetically controlled capsule endoscopy. DSomNN: Deep self-organizing competitive neural network.

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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

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 [DOI: 10.12998/wjcc.126704]

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