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Articles Published Processes
2/3/2026 6:28:11 AM | Browse: 1 | Download: 1
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Received |
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2025-09-05 03:13 |
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Peer-Review Started |
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2025-09-05 03:13 |
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First Decision by Editorial Office Director |
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2025-09-09 09:27 |
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Return for Revision |
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2025-09-09 09:27 |
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Revised |
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2025-09-11 22:03 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-11-26 02:34 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-11-26 07:02 |
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Articles in Press |
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2025-11-26 07:02 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-01-26 08:26 |
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Publish the Manuscript Online |
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2026-02-03 06:28 |
| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Systematic Reviews |
| Article Title |
Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Sami Akbulut and Cemil Colak |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Sami Akbulut, FACS, MD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10 Km, Malatya 44280, Türkiye. akbulutsami@gmail.com |
| Key Words |
Hepatocellular carcinoma; Deep learning; Convolutional Neural Networks; Recurrent Neural Networks; Transformers; Medical imaging; Artificial intelligence efficiency |
| Core Tip |
Hepatocellular carcinoma remains a major cause of cancer-related mortality worldwide, with limited sensitivity of current screening tools for early detection. Deep learning (DL) offers great promise, but computational demands often hinder its clinical use. This review highlights advances in efficiency-oriented strategies, including lightweight architectures, pruning, quantization, and multimodal integration, which enable smaller and faster models without major loss of accuracy. By emphasizing validation, fairness audits, regulatory alignment, and workflow integration, we provide guidance for developing explainable and efficient DL solutions that are clinically deployable and impactful. |
| Publish Date |
2026-02-03 06:28 |
| 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 |
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