Learning classification of big medical imaging data based on partial differential equation

Abstract

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.