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Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

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  • Natinai Jinsakul

    (Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
    Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Cheng-Fa Tsai

    (Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Chia-En Tsai

    (Department of Biochemistry and Molecular Biology, National Cheng Kung University, Tainan 701, Taiwan)

  • Pensee Wu

    (Center for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele ST5 5BG, UK)

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.

Suggested Citation

  • Natinai Jinsakul & Cheng-Fa Tsai & Chia-En Tsai & Pensee Wu, 2019. "Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening," Mathematics, MDPI, vol. 7(12), pages 1-21, December.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1170-:d:293513
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    References listed on IDEAS

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    1. Saeed-Ul Hassan & Mubashir Imran & Sehrish Iqbal & Naif Radi Aljohani & Raheel Nawaz, 2018. "Deep context of citations using machine-learning models in scholarly full-text articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1645-1662, December.
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