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Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps

1
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2
Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(2), 45; https://doi.org/10.3390/fi11020045
Received: 11 January 2019 / Revised: 9 February 2019 / Accepted: 13 February 2019 / Published: 15 February 2019
(This article belongs to the Special Issue Innovative Topologies and Algorithms for Neural Networks)
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Abstract

The tooth-marked tongue is an important indicator in traditional Chinese medicinal diagnosis. However, the clinical competence of tongue diagnosis is determined by the experience and knowledge of the practitioners. Due to the characteristics of different tongues, having many variations such as different colors and shapes, tooth-marked tongue recognition is challenging. Most existing methods focus on partial concave features and use specific threshold values to classify the tooth-marked tongue. They lose the overall tongue information and lack the ability to be generalized and interpretable. In this paper, we try to solve these problems by proposing a visual explanation method which takes the entire tongue image as an input and uses a convolutional neural network to extract features (instead of setting a fixed threshold artificially) then classifies the tongue and produces a coarse localization map highlighting tooth-marked regions using Gradient-weighted Class Activation Mapping. Experimental results demonstrate the effectiveness of the proposed method. View Full-Text
Keywords: tooth-marked tongue; convolutional neural network; gradient-weighted class activation maps tooth-marked tongue; convolutional neural network; gradient-weighted class activation maps
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, Y.; Dai, S.; Li, J.; Zhang, Y.; Li, X. Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps. Future Internet 2019, 11, 45.

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