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

Jinsakul, Natinai; Tsai, Cheng-Fa; Tsai, Chia-En; Wu, Pensee

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.

Citation

Jinsakul, N., Tsai, C., Tsai, C., & Wu, P. (2019). Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening. Mathematics, 7(12), Article 1170. https://doi.org/10.3390/math7121170

• Journal Article Type: Article
• Acceptance Date: Nov 20, 2019
• Publication Date: Dec 3, 2019
• Journal: Mathematics
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 12
• Article Number: 1170
• DOI: https://doi.org/10.3390/math7121170
• Keywords: deep learning, Xception, convolutional neural network, Swish activation function, colorectal polyps, preliminary screening, image classification, topogram image
• Publisher URL: https://doi.org/10.3390/math7121170