| Summary: | Optical coherence tomography (OCT) shows an important role in the diagnosis of cardiovascular diseases and the detection and intervention of vulnerable plaques. Clinical diagnosis of vulnerable plaques in cardiovascular system based on optical coherence tomography mainly relies on the manual analysis of vulnerable plaques in images by cardiovascular physicians. This analysis process is prone to subjective misjudgments and heavy workload. Studying the recognition technology of cardiovascular vulnerable plaque image will greatly improve the accuracy of diagnosis and reduce the workload of cardiovascular physicians, which is an effective way to achieve efficient diagnosis and treatment. In view of the recognition of cardiovascular vulnerable plaque medical images, a model based on convolution neural network (CNN) recognition is constructed. The CNN is used to learn the features of different levels from the original input OCT images. At the same time, several decision-making levels are designed. These decision-making levels can classify OCT images according to different feature maps, and finally make final recognition decisions according to the classification results. The experimental results on the clinical data set labeled by doctors show that the classification and recognition model of cardiovascular vulnerable plaque OCT image based on CNN has a high recognition rate. Making full use of the multilevel features of convolutional neural networks can effectively classify and recognize the OCT images of vulnerable cardiovascular plaques, provide support for the clinical diagnosis of cardiovascular diseases, and have great significance for the early intervention and prevention of cardiovascular diseases.
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