Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks

doi:10.3748/wjg.v32.i15.116105

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World Journal of Gastroenterology

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1007-9327

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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 Gastroenterol. Apr 21, 2026; 32(15): 116105
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116105

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Figure 1 Overall schematic of the proposed convolutional neural network-long short-term memory framework for capsule endoscopy bleeding detection. The diagram illustrates the workflow from image acquisition, preprocessing, and convolutional neural network-based spatial feature extraction to long short-term memory-based temporal modeling and final prediction. LSTM: Long short-term memory; CNN: Convolutional neural network.

Citation: Kuo HY, Lee KH, Chou CK, Mukundan A, Karmakar R, Chen TH, Wang TL, Liu PH, Wang HC. Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks. World J Gastroenterol 2026; 32(15): 116105
URL: https://www.wjgnet.com/1007-9327/full/v32/i15/116105.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i15.116105

Kuo HY, Lee KH, Chou CK, Mukundan A, Karmakar R, Chen TH, Wang TL, Liu PH, Wang HC. Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks. World J Gastroenterol 2026; 32(15): 116105 [DOI: 10.3748/wjg.v32.i15.116105]

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