北京生物醫(yī)學工程

基于AdaBoost級聯(lián)框架的舌色分類

Tongue color classification based on AdaBoost cascade framework

作者：王奕然張新峰

單位：北京工業(yè)大學信息學部（北京 100124）

分類號：R318

出版年·卷·期（頁碼）：2020·39·1（8-14）

摘要：

目的基于圖像處理的舌質(zhì)顏色分析是中醫(yī)舌診現(xiàn)代化的重要內(nèi)容，提高舌色的正確識別率是其中的關鍵問題。本文使用集成學習的分類方法來探討舌色分類問題，以達到客觀，準確地識別中醫(yī)舌色的目的。方法首先通過AdaBoost算法對舌圖像進行初步分類，再將該算法與級聯(lián)框架進行結(jié)合，然后通過“一對其余”的方法將AdaBoost從二分類擴展到多類來完成舌質(zhì)顏色的分析。最后通過實驗進行了驗證，并與其他方法所得出的結(jié)果進行對比。結(jié)果針對各類舌質(zhì)顏色分類問題，使用隨機森林與傳統(tǒng)的AdaBoost分類器進行分類的正確率分別在78.0%-90.2%與89.4%-95.5%區(qū)間，而基于AdaBoost級聯(lián)框架的分類器的各類舌質(zhì)分類正確率在93.0%-98.7%之間。結(jié)論基于AdaBoost級聯(lián)框架的舌質(zhì)顏色分類方法與其他經(jīng)典方法相比，具有較高的正確分類率，為基于圖像處理的中醫(yī)舌診輔助診斷奠定了一定的基礎。

Objective The color analysis of tongue based on image processing is an important part of the automatic analysis of tongue images in Chinese medicine. It is the key to improve the correct recognition rate of tongue color. This paper uses the classification method of integrated learning to explore the problem of tongue color classification [no, nuclear, and corresponding English] to achieve objective and accurate identification of TCM tongue color.Methods Firstly, the tongue image is preliminarily classified by AdaBoost algorithm, and then the algorithm is combined with the cascade framework. AdaBoost can be extended from two categories to multiple classes by a "one pair of rest" approach. In this way, the analysis of the color of the tongue can be achieved.Finally, the experiment is carried out and the results obtained by other methods were compared. ResultsFor various tongue color classification problems, the accuracy of classification using random forest and traditional AdaBoost classifiers is in the range of 78.0% -90.2% and 89.4% -95.5%, respectively, and the types of classifiers based on the AdaBoost cascade framework The accuracy of tongue classification is between 93.0% -98.7%.Conclusions Compared with other classical methods, the tongue color classification method based on AdaBoost cascading framework has a higher correct classification rate, which lays a foundation for the diagnosis of TCM tongue diagnosis based on image processing.

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