Jinsakul, N, Tsai, C-F, Tsai, C-E and Wu, P ORCID: https://orcid.org/0000-0003-0011-5636 (2019) Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening. Mathematics, 7 (12). 1170 -1170.

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Abstract

One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes.

Item Type: Article
Additional Information: This is the final published version of the article (version of record). It first appeared online via MDPI at https://doi.org/10.3390/math7121170 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: deep learning, Xception, convolutional neural network, Swish activation function, colorectal polyps, preliminary screening, image classification, topogram image
Subjects: R Medicine > R Medicine (General) > R735 Medical education. Medical schools. Research
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Faculty of Medicine and Health Sciences > Institute for Science and Technology in Medicine
Depositing User: Symplectic
Date Deposited: 06 Dec 2019 16:48
Last Modified: 06 Dec 2019 16:55
URI: https://eprints.keele.ac.uk/id/eprint/7338

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