Title: Deep-DSP: deep convolutional network with double spatial pyramid for tongue image segmentation

Authors: Yuhan Chen; Qingfeng Li; Dongxin Lu; Wende Ke; Wenming Cui; Lei Kou

Addresses: School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China ' Health Management System Engineering Center, School of Public Health, Hangzhou Normal University, Hangzhou, China ' Health Management System Engineering Center, School of Public Health, Hangzhou Normal University, Hangzhou, China ' Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China ' China United Test and Evaluation (Qingdao) Co., Ltd., Qingdao, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, China

Abstract: In traditional Chinese medicine diagnosis, experienced Chinese medicine doctors can understand the potential symptoms of patients by diagnosing their tongue images. With the continuous development of computer science and artificial intelligence, neural networks are gradually being used in the image processing and feature extraction. The intelligent tongue diagnosis combining traditional Chinese medicine with neural networks was constructed to automatically improve the diagnose accuracy. The dataset contains 556 no-background tongue images. The deep convolutional network with double spatial pyramid (Deep-DSP) for tongue segmentation was constructed, in which the spatial pyramid block was embedded into the encoder-decoder architecture. Noteworthy, a novel double spatial pyramid architecture was applied for multiple encoding states feature learning. Experiments showed that the deep-DSP performed more excellently in tongue segmentation and than that of state-of-the-art methods according to the accuracy, precision, recall, harmonic measure and specificity.

Keywords: convolutional neural network; CNN; tongue dataset; tongue segmentation.

DOI: 10.1504/IJBIC.2024.137916

International Journal of Bio-Inspired Computation, 2024 Vol.23 No.3, pp.157 - 167

Received: 19 Apr 2023
Accepted: 16 Nov 2023
Published online: 08 Apr 2024 *

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