Copyright
©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Dec 15, 2019; 11(12): 1218-1230
Published online Dec 15, 2019. doi: 10.4251/wjgo.v11.i12.1218
Published online Dec 15, 2019. doi: 10.4251/wjgo.v11.i12.1218
Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review
Samy A Azer, Department of Medical Education, King Saud University College of Medicine, Riyadh 11461, Saudi Arabia
Author contributions: The author SAA created the idea of the review, generated the rationale and the research question, designed the project, searched the databases, analysed the findings, created the tables, wrote the manuscript, and approved the final manuscript.
Supported by the College of Medicine Research Centre, Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia .
Conflict-of-interest statement: The author declares that he has no competing interests.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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/
Corresponding author: Samy A Azer, FACG, Professor of Medical Education, Gastroenterologist, Department of Medical Education, King Saud University College of Medicine, P O Box 2925, Riyadh 11461, Saudi Arabia. azer2000@optusnet.com.au
Telephone: +966-11-8066393 Fax: +966-11-4699174
Received: March 2, 2019
Peer-review started: March 4, 2019
First decision: June 5, 2019
Revised: July 9, 2019
Accepted: October 3, 2019
Article in press: October 3, 2019
Published online: December 15, 2019
Peer-review started: March 4, 2019
First decision: June 5, 2019
Revised: July 9, 2019
Accepted: October 3, 2019
Article in press: October 3, 2019
Published online: December 15, 2019
Core Tip
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 recognise 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 CNNs and their clinical performance.