Traditional big medical image data classification methods are mostly based on the change of image gray features, extract edge and contour feature information, or perform conversion between medical image coordinate sets. However, the algorithms are complicated, real-time performance is poor, classification speed is slow, and accuracy is low. This paper proposes a classification study of big medical image data based on partial differential equations by combined with deep learning algorithms, and uses partial differential equations in big medical image processing to extract the texture features of medical images. Moreover, according to the texture features of the medical image contrast modulation, this paper filters out the image noise interference. Based on the depth learning algorithm, the image distance stratification, the target object size, the fit and other information, the accurate classification of big medical image data is realized. Experimental results show that the proposed classification method has high efficiency, low error rate, good real-time performance and robustness.
Partial differential equations Big medical image data Medical images Texture features Contrast modulation
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This work is supported by the following programs. (1) Sichuan Science and technology projects (18ZDYF2517); (2) Zigong science and Technology Bureau (2016DZ11); (3) Sichuan Provincial Academician (Expert) Workstation (2015YSGZZ04 and 2016YSGZZ02); Key Laboratory Higher Education of Sichuan Province for Enterprise Informationalization and IOT (2015WZJ01); Sichuan Provincial Key research Base of Intelligent Tourism (ZHY17-02).
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