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 [PMID: 33024753 DOI: 10.12998/wjcc.v8.i18.3971]
Corresponding Author of This Article
Jun Ma, MD, Doctor, Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, No. 1 Nanbaixiang Street, Wenzhou 325000, Zhejiang Province, China. majun@wzhospital.cn
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
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/
World J Clin Cases. Sep 26, 2020; 8(18): 3971-3977 Published online Sep 26, 2020. doi: 10.12998/wjcc.v8.i18.3971
Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors
Wei-Bo Mao, Jia-Yu Lyu, Deep K Vaishnani, Yu-Man Lyu, Wei Gong, Xi-Ling Xue, Yang-Ping Shentu, Jun Ma
Wei-Bo Mao, Wei Gong, Department of Pathology, Lishui Hospital of Zhejiang University, Lishui Central Hospital, Lishui 323000, Zhejiang Province, China
Jia-Yu Lyu, Xi-Ling Xue, Department of Psychiatry, The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China
Deep K Vaishnani, School of International Studies, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
Yu-Man Lyu, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
Yang-Ping Shentu, Jun Ma, Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, China
Author contributions: Mao WB and Lyu JY contributed equally to this work; Vaishnani DK collected the history and special feature of machine learning and the recent trend of computer-aided detection and diagnosis for endoscopic and ultrasonic images; Lyu YM and Shentu YP worked on deep learning and the background and significance of diagnostic artificial intelligence; Ma J worked on artificial neural network and deep learning.
Conflict-of-interest statement: The authors declare no conflict of interests for this article.
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/
Corresponding author: Jun Ma, MD, Doctor, Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, No. 1 Nanbaixiang Street, Wenzhou 325000, Zhejiang Province, China. majun@wzhospital.cn
Received: February 28, 2020 Peer-review started: February 28, 2020 First decision: April 22, 2020 Revised: May 10, 2020 Accepted: June 29, 2020 Article in press: June 29, 2020 Published online: September 26, 2020 Processing time: 206 Days and 16.6 Hours
Abstract
As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors. In this article, we introduce the development, working principle, and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.
Core Tip: This paper describes the application of artificial neural networks (ANNs) in the detection and diagnosis of gastrointestinal and liver tumors. We review the artificial intelligence, ANNs and their ability, parallel processing capability, and nonlinear processing. We also discuss the working principle and characteristics of ANNs.
