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Open AccessArticle

StoolNet for Color Classification of Stool Medical Images

by Ziyuan Yang 1,2, Lu Leng 1,* and Byung-Gyu Kim 3,*
School of Software, Nanchang Hangkong University, Nanchang 330063, China
School of Information Engineering, Nanchang University, Nanchang 330031, China
Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Korea
Authors to whom correspondence should be addressed.
Electronics 2019, 8(12), 1464;
Received: 9 November 2019 / Revised: 22 November 2019 / Accepted: 24 November 2019 / Published: 2 December 2019
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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. View Full-Text
Keywords: StoolNet; convolutional neural network; color classification; stool medical image StoolNet; convolutional neural network; color classification; stool medical image
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Yang, Z.; Leng, L.; Kim, B.-G. StoolNet for Color Classification of Stool Medical Images. Electronics 2019, 8, 1464.

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