To circumvent the subjective and qualitative problems of traditional tongue diagnosis, we present a novel computer aided tongue diagnosis system (CATDS). In this system, a standard acquisition device as well as a new color correction method is utilized to capture qualified tongue images. The system is constituted by five components: User Interface Module, Acquisition Module, Tongue Image Database, Image Preprocessing Module and Diagnosis Engine. In contrast to existing CATDS, the proposed system aims to establish the relationship between quantitative features and diseases via the Bayesian networks. System tests are carried out on a group of 544 patients affected by 9 common diseases and 56 healthy volunteers. The results show that the system can properly identify six groups: healthy, pulmonary heart disease, appendicitis, gastritis, pancreatitis and bronchitis with accuracy higher than 75%. Moreover, the execution time for the whole diagnosis process including image preprocessing and diagnosis is less than 5 seconds.

In this paper, we proposed a kernel differenceweighted k-nearest neighbor classifier (KDF-WKNN)

This paper proposes a new algorithm to classify pulse waveforms based on discrete wavelet network. This paper selects 4-order discrete Daubechies wavelet as the wavelet node of this wavelet network to classify six pulse patterns distinctive in shape. 600 pulse records are used to train this wavelet network and 300 pulse records are used to test the classifier's performance. The test results show that this approach has 83% agreement rate with the experienced experts. Compared with traditional classification methods, it needs not the experience in feature extraction.

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