@article{scholars17385,
            year = {2022},
           pages = {735--743},
         journal = {Lecture Notes in Electrical Engineering},
       publisher = {Springer Science and Business Media Deutschland GmbH},
             doi = {10.1007/978-981-16-2183-3{$_7$}{$_1$}},
          volume = {758},
            note = {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},
           title = {Computer-Aided Diagnostic Tool for Classification of Colonic Polyp Assessment},
            isbn = {9789811621826},
          author = {Liew, W. S. and Tang, T. B. and Lu, C.-K.},
            issn = {18761100},
        abstract = {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. {\^A}{\copyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142734267&doi=10.1007\%2f978-981-16-2183-3\%5f71&partnerID=40&md5=2ceccd1fbdad595a5a637f310e56b7ad},
        keywords = {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}
}