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Articles Published Processes
12/10/2019 2:02:23 AM | Browse: 725 | Download: 1231
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Received |
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2019-03-02 07:01 |
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Peer-Review Started |
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2019-03-04 10:42 |
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To Make the First Decision |
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2019-06-05 06:26 |
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Return for Revision |
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2019-06-27 02:26 |
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Revised |
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2019-07-09 13:51 |
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Second Decision |
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2019-09-24 08:03 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Company Editor-in-Chief |
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2019-10-04 00:24 |
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Articles in Press |
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2019-10-04 00:24 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2019-10-11 18:12 |
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Typeset the Manuscript |
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2019-11-21 07:41 |
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Publish the Manuscript Online |
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2019-12-10 02:02 |
| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2019. 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 |
Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Samy A Azer |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| This work was funded by the College of Medicine Research Center, Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia |
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| Corresponding Author |
Samy A Azer, FACG, Professor, Department of Medical Education, King Saud University College of Medicine, P O Box 2925, Riyadh 11461, Riyadh, Saudi Arabia. azer2000@optusnet.com.au |
| Key Words |
Deep learning; Convolutional neural network; Hepatocellular carcinoma; Liver masses; Liver cancer; Medical imaging |
| Core Tip |
Artificial intelligence, such as convolutional neural networks (CNNs) have been used in the interpretation of images, including pathology and radiology images with potential application in the diagnosis of hepatocellular cancer (HCC) and liver masses. CNN, a machine-learning algorithm similar to deep learning, has demonstrated its capability to recognize specific features that can detect pathological lesions. The primary aim of this review is to assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer. The second aim is to evaluate the accuracy level of the CNNs and their clinical performance. |
| Publish Date |
2019-12-10 02:02 |
| Citation |
Azer SA. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World J Gastrointest Oncol 2019; 11(12): 1182-1192 |
| URL |
https://www.wjgnet.com/1948-5204/full/v11/i12/1218.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v11.i12.1218 |
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