Single Shot Tooth Mark Detector for Tongue Diagnosis in Traditional Chinese Medicine

Abstract

AbstractThe Tooth mark is an important attribute of tongue diagnosis in Traditional Chinese Medicine (TCM). The recognition results obtained by current methods heavily rely on the results of tongue image segmentation. To solve the problem, we regarded the tooth marks recognition as a task of object detection and improved the original single shot detector (SSD) to detect the tooth marks. We removed the last two prediction layers of SSD and set the aspect ratios of the prior box to 1 based on the statistical data of the size and aspect ratios of tooth mark regions. Then we designed the multiple feature fusion module to combine the multi-scale features and embedded them hierarchically into the network to transfer the semantic information from deep layers to shallow layers. Furthermore, we also developed a feature enhancement module to improve the distinctiveness of features. The experimental results showed that the proposed method achieved 96.8% in terms of accuracy, which is significantly better than the current methods.KeywordsTongue diagnosisTraditional Chinese medicineObject detectionConvolutional neural network