<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Computer-Aided Diagnostic Tool for Classification of Colonic Polyp Assessment"^^ . "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."^^ . "2022" . . "758" . . "Springer Science and Business Media Deutschland GmbH"^^ . . "Springer Science and Business Media Deutschland GmbH"^^ . . . "Lecture Notes in Electrical Engineering"^^ . . . "18761100" . . . . . . . . . . . . . "T.B."^^ . "Tang"^^ . "T.B. Tang"^^ . . "W.S."^^ . "Liew"^^ . "W.S. Liew"^^ . . "C.-K."^^ . "Lu"^^ . "C.-K. Lu"^^ . . . . . "HTML Summary of #17385 \n\nComputer-Aided Diagnostic Tool for Classification of Colonic Polyp Assessment\n\n" . "text/html" . .