IEEE Trans Cybern. 2019 Feb;49(2):380-387. doi: 10.1109/TCYB.2017.2772289. Epub 2018 Jan 30.
Tooth-marked tongue or crenated tongue can provide valuable diagnostic information for traditional Chinese Medicine doctors. However, tooth-marked tongue recognition is challenging. The characteristics of different tongues are multiform and have a great amount of variations, such as different colors, different shapes, and different types of teeth marks. The regions of teeth mark only appear along the lateral borders. Most existing methods make use of concave regions information to classify the tooth-marked tongue which leads to inconstant performance when the region of teeth mark is not concave. In this paper, we try to solve these problems by proposing a three-stage approach which first makes use of concavity information to propose the suspected regions, then use a convolutional neural network to extract deep features and at last use a multiple-instance classifier to make the final decision. Experimental results demonstrate the effectiveness of the proposed method.
齿痕舌或裂舌可为中医医生提供有价值的诊断信息。然而，齿痕舌识别具有挑战性。不同舌头的特征是多样的，变化很大，例如不同的颜色、不同的形状和不同类型的齿痕。齿痕区域仅出现在侧缘。现有的大多数方法都利用凹区域信息来对齿痕舌进行分类，这导致在齿痕区域不凹时性能不稳定。在本文中，我们尝试通过提出一个三阶段的方法来解决这些问题，该方法首先利用凹度信息来提出可疑区域，然后使用卷积神经网络提取深度特征，最后使用多实例分类器做出最终决策。实验结果证明了所提出方法的有效性。
