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StoolNet for Color Classification of Stool Medical Images

Authors
Yang, ZiyuanLeng, LuKim, Byung-Gyu
Issue Date
Dec-2019
Publisher
MDPI
Keywords
StoolNet; convolutional neural network; color classification; stool medical image
Citation
ELECTRONICS, v.8, no.12
Journal Title
ELECTRONICS
Volume
8
Number
12
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/2705
DOI
10.3390/electronics8121464
ISSN
1450-5843
2079-9292
Abstract
The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians' heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.
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공과대학 (인공지능공학부)
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