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6/28/2020 11:09:09 PM | Browse: 206 | Download: 357
Publication Name World Journal of Clinical Cases
Manuscript ID 55040
Country/Territory China
Category Medicine, Research & Experimental
Manuscript Type Minireviews
Article Title Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors
Manuscript Source Unsolicited Manuscript
All Author List Wei-Bo Mao, Jia-Yu Lyu, Deep k Vaishnani, Yu-Man Lyu, Wei Gong, Xi-Ling Xue, Yang-Ping Shentu and Jun Ma
Funding Agency and Grant Number
Corresponding Author Jun Ma, MD, Doctor, Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Street 1, Wenzhou 325000, Zhejiang Province, China. majun@wzhospital.cn
Key Words Artificial neural network; Deep learning; Gastrointestinal tumor; Tumor detection; Artificial intelligence;
Core Tip This paper describes the application of artificial neural networks (ANN) in the detection and diagnosis of gastrointestinal and liver tumors, which investigate artificial intelligence, ANNs and their ability, parallel processing capability, and nonlinear processing. They occur widely in the early detection and diagnosis of tumorous. The working principle and the characteristics of the ANN introduced in this research paper.
Citation Mao WB, Lyu JY, Vaishnani DK, Lyu YM, Gong W, Xue XL, Shentu YP, Ma J. Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors. World J Clin Cases 2020; 8(18): 3971-3977
Received
2020-02-28 10:43
Peer-Review Started
2020-02-28 10:46
To Make the First Decision
Return for Revision
2020-04-24 12:06
Revised
2020-05-10 04:44
Second Decision
2020-06-28 10:08
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2020-06-28 23:09
Articles in Press
2020-06-28 23:09
Publication Fee Transferred
Edit the Manuscript by Language Editor
2020-08-28 08:39
Typeset the Manuscript
2020-09-10 14:00
ISSN 2307-8960 (online)
Open Access 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: http://creativecommons.org/licenses/by-nc/4.0/
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