eprintid: 17385
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/73/85
datestamp: 2023-12-19 03:23:47
lastmod: 2023-12-19 03:23:47
status_changed: 2023-12-19 03:07:58
type: article
metadata_visibility: show
creators_name: Liew, W.S.
creators_name: Tang, T.B.
creators_name: Lu, C.-K.
title: Computer-Aided Diagnostic Tool for Classification of Colonic Polyp Assessment
ispublished: pub
keywords: Computer aided diagnosis; Computer aided instruction; Convolution; Convolutional neural networks; Deep neural networks; Diseases; Principal component analysis; Transfer learning, Colonic polyps; Colorectal cancer; Computer aided diagnostics; Convolutional neural network; Deep convolutional neural network; Diagnostics tools; Polyp; Pre-processing; Support vectors machine; Transfer learning, Support vector machines
note: cited By 1; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
abstract: Colorectal cancer is the third most common malignancy and the fourth leading cause of cancer-related deaths worldwide. This paper presents a combination of techniques (e.g., pre-processing, transfer learning, principal component analysis, and support vector machine) to detect the polyp during colonoscopy. In particular, we carefully choose the pre-trained deep convolutional neural networks (i.e., AlexNet, GoogLeNet, ResNet-50, and VGG-19) according to their performance extracting features. A publicly available database, Kvasir, is used to train and test the detection model. The result indicates that to use ResNet-50 as a pre-trained network provides the best results among the rest. Our proposed model achieves an accuracy of 99.39, and its sensitivity and specificity are 99.39, 99.41, and 99.38, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142734267&doi=10.1007%2f978-981-16-2183-3_71&partnerID=40&md5=2ceccd1fbdad595a5a637f310e56b7ad
id_number: 10.1007/978-981-16-2183-3₇₁
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 758
pagerange: 735-743
refereed: TRUE
isbn: 9789811621826
issn: 18761100
citation:   Liew, W.S. and Tang, T.B. and Lu, C.-K.  (2022) Computer-Aided Diagnostic Tool for Classification of Colonic Polyp Assessment.  Lecture Notes in Electrical Engineering, 758.  pp. 735-743.  ISSN 18761100