BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
12/10/2019 2:02:23 AM | Browse: 135 | Download: 79
Publication Name World Journal of Gastrointestinal Oncology
Manuscript ID 46994
Country/Territory Saudi Arabia
Received
2019-03-02 07:01
Peer-Review Started
2019-03-04 10:42
To Make the First Decision
2019-06-05 06:26
Return for Revision
2019-06-27 02:26
Revised
2019-07-09 13:51
Second Decision
2019-09-24 08:03
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2019-10-04 00:24
Articles in Press
2019-10-04 00:24
Publication Fee Transferred
Edit the Manuscript by Language Editor
2019-10-11 18:12
Typeset the Manuscript
2019-11-21 07:41
Publish the Manuscript Online
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
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Gastroenterology and Hepatology
Manuscript Type Systematic Reviews
Article Title Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review
Manuscript Source Invited Manuscript
All Author List Samy A Azer
ORCID
Author(s) ORCID Number
Samy A Azer http://orcid.org/0000-0001-5638-3256
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
Corresponding author Samy A Azer, FACG, Professor, Gastroenterologist, Department of Medical Education, King Saud University College of Medicine, P O Box 2925, Riyadh 11461, Riyadh, Saudi Arabia. azer2000@optusnet.com.au
Keywords 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
Full Article (PDF) WJGO-11-1218.pdf
Full Article (Word) WJGO-11-1218.docx
Manuscript File FP7035_ce5_CE1MS-edit2-2.docx
Answering Reviewers 46994-Answering reviewers.pdf
Audio Core Tip 46994-Audio core tip.mp3
Biostatistics Review Certificate 46994-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form 46994-Conflict-of-interest statement.pdf
Copyright License Agreement 46994-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s) 46994-Grant application form(s).pdf
Peer-review Report 46994-Peer-review(s).pdf
Scientific Misconduct Check 46994-Scientific misconduct check.pdf
Scientific Editor Work List 46994-Scientific editor work list.pdf