Establishment and validation of a multimodal model using deep learning for the recognition of early colorectal cancer

doi:10.4251/wjgo.123187

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World Journal of Gastrointestinal Oncology

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1948-5204

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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 Gastrointest Oncol. Nov 15, 2026; 18(11): 123187
Published online Nov 15, 2026. doi: 10.4251/wjgo.123187

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Figure 3 The Gradient-weighted Class Activation Mapping algorithms for three models. The right side shows the weight maps generated by the Gradient-weighted Class Activation Mapping algorithm for the VGG19, ResNet50, and InceptionV3 models, respectively. The color scale ranges from blue to red, with redder regions indicating greater importance to the model’s decision.

Citation: Dong WH, Ling X, Chen XD, Qian JP, Jin Y, Gu YJ. Establishment and validation of a multimodal model using deep learning for the recognition of early colorectal cancer. World J Gastrointest Oncol 2026; 18(11): 123187
URL: https://www.wjgnet.com/1948-5204/full/v18/i11/123187.htm
DOI: https://dx.doi.org/10.4251/wjgo.123187

Dong WH, Ling X, Chen XD, Qian JP, Jin Y, Gu YJ. Establishment and validation of a multimodal model using deep learning for the recognition of early colorectal cancer. World J Gastrointest Oncol 2026; 18(11): 123187 [DOI: 10.4251/wjgo.123187]

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