relation: https://khub.utp.edu.my/scholars/17385/
title: Computer-Aided Diagnostic Tool for Classification of Colonic Polyp Assessment
creator: Liew, W.S.
creator: Tang, T.B.
creator: Lu, C.-K.
description: 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.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022
type: Article
type: PeerReviewed
identifier:   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     
relation: 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
relation: 10.1007/978-981-16-2183-3₇₁
identifier: 10.1007/978-981-16-2183-3₇₁