relation: https://khub.utp.edu.my/scholars/14799/
title: Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches
creator: Liew, W.S.
creator: Tang, T.B.
creator: Lin, C.-H.
creator: Lu, C.-K.
description: Background and Objective: The increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g. sensitivity). Therefore, a new CAD tool is developed in this study to detect colonic polyps accurately. Methods: In this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps. Results: The proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10, 98.82, 99.37, and 99.38, respectively. Conclusions: These results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection. © 2021
publisher: Elsevier Ireland Ltd
date: 2021
type: Article
type: PeerReviewed
identifier:   Liew, W.S. and Tang, T.B. and Lin, C.-H. and Lu, C.-K.  (2021) Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches.  Computer Methods and Programs in Biomedicine, 206.   ISSN 01692607     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105593805&doi=10.1016%2fj.cmpb.2021.106114&partnerID=40&md5=9c0357281484fc0b8733c207d0ba55df
relation: 10.1016/j.cmpb.2021.106114
identifier: 10.1016/j.cmpb.2021.106114