Skip to main content

Research Repository

Advanced Search

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

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


Authors

Natinai Jinsakul

Cheng-Fa Tsai

Chia-En Tsai



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.

Journal Article Type Article
Acceptance Date Nov 20, 2019
Publication Date Dec 3, 2019
Publicly Available Date Mar 29, 2024
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

Files




You might also like



Downloadable Citations