%P 735-743 %T Computer-Aided Diagnostic Tool for Classification of Colonic Polyp Assessment %I Springer Science and Business Media Deutschland GmbH %V 758 %A W.S. Liew %A T.B. Tang %A C.-K. Lu %D 2022 %R 10.1007/978-981-16-2183-3₇₁ %O 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 %L scholars17385 %J Lecture Notes in Electrical Engineering %K 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 %X 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.