Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation

doi:10.4251/wjgo.v18.i2.113870

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Feb 15, 2026 (publication date) through Feb 3, 2026

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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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World J Gastrointest Oncol. Feb 15, 2026; 18(2): 113870
Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.113870

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Figure 1 Efficiency-performance trade-offs in deep learning models for hepatocellular carcinoma. A: Performance vs model parameters; B: Performance vs computational cost. CNN: Convolutional neural network; RNN: Recurrent neural network; AUC: Area under curve; GFLOPs: Giga floating point operations.

Citation: Akbulut S, Colak C. Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation. World J Gastrointest Oncol 2026; 18(2): 113870
URL: https://www.wjgnet.com/1948-5204/full/v18/i2/113870.htm
DOI: https://dx.doi.org/10.4251/wjgo.v18.i2.113870