Journal
ELECTRONICS
Volume 8, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/electronics8121464
Keywords
StoolNet; convolutional neural network; color classification; stool medical image
Categories
Funding
- National Natural Science Foundation of China [61866028, 61763033, 61662049, 61663031, 61866025]
- Foundation of China Scholarship Council [CSC201908360075]
- Key Program Project of Research and Development (Jiangxi Provincial Department of Science and Technology) [20171ACE50024, 20161BBE50085]
- Construction Project of Advantageous Science and Technology Innovation Team in Jiangxi Province [20165BCB19007]
- Application Innovation Plan (Ministry of Public Security of P. R. China) [2017YYCXJXST048]
- Open Foundation of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition [ET201680245, TX201604002]
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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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