eprintid: 14799 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/47/99 datestamp: 2023-11-10 03:29:23 lastmod: 2023-11-10 03:29:23 status_changed: 2023-11-10 01:57:50 type: article metadata_visibility: show creators_name: Liew, W.S. creators_name: Tang, T.B. creators_name: Lin, C.-H. creators_name: Lu, C.-K. title: Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches ispublished: pub keywords: Classification (of information); Computer aided diagnosis; Convolutional neural networks; Deep neural networks; Diseases; Endoscopy; Image enhancement; Median filters; Network architecture; Principal component analysis, Adaboost ensemble learning; Colonic polyp detection; Colonic polyps; Colorectal cancer; Computer-aided; Deep residual network; Diagnosis tools; Endoscopic image; Polyp; Principal-component analysis, Adaptive boosting, Article; automation; cancer incidence; classification; colon polyp; colonoscopy; colorectal cancer; computer assisted diagnosis; contrast enhancement; convolutional neural network; correlation coefficient; diagnostic accuracy; diagnostic test accuracy study; dimensionality reduction; early cancer diagnosis; feature extraction; human; learning algorithm; mortality rate; principal component analysis; residual neural network; sensitivity and specificity; artificial intelligence; colon polyp; diagnostic imaging; machine learning, Artificial Intelligence; Colonic Polyps; Diagnosis, Computer-Assisted; Humans; Machine Learning; Neural Networks, Computer note: cited By 22 abstract: 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 date: 2021 publisher: Elsevier Ireland Ltd official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105593805&doi=10.1016%2fj.cmpb.2021.106114&partnerID=40&md5=9c0357281484fc0b8733c207d0ba55df id_number: 10.1016/j.cmpb.2021.106114 full_text_status: none publication: Computer Methods and Programs in Biomedicine volume: 206 refereed: TRUE issn: 01692607 citation: 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