Abstract
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.
Highlights
More than 20 million people worldwide experience acute heart disease such as acute coronary syndrome (ACS) and/or sudden cardiac death each year
Some statistical conclusions are obtained from statistical results of convolution neural network (CNN) structures with smaller convolution kernels: (1) Increasing the depth of the network can improve the accuracy; (2) the accuracy can be improved by increasing the number of element planes; (3) in terms of obtaining higher accuracy, the method of adding a convolutional layer is more effective than adding a fully connected layer
Effective technical means are needed to screen for heart disease in persons who may be affected, and to diagnose and treat patients in time
Summary
More than 20 million people worldwide experience acute heart disease such as acute coronary syndrome (ACS) and/or sudden cardiac death each year. Some statistical conclusions are obtained from statistical results of CNN structures with smaller convolution kernels: (1) Increasing the depth of the network can improve the accuracy; (2) the accuracy can be improved by increasing the number of element planes; (3) in terms of obtaining higher accuracy, the method of adding a convolutional layer is more effective than adding a fully connected layer. Kai-Ming and Jian [18] explored how to balance the depth, number of features, and size of the convolution-kernel in the CNN network-structure in terms of computational complexity and time. The CNN network structure is set to reduce the number of feature planes when increasing the network-depth, while the size of the convolution-kernel remains unchanged. Where y ∈ Rm×1 represents the output of the neuron, x ∈ Rn×1 represents the input of the neuron, W ∈ Rn×m represents the weight of the neuron, b is the bias term, and is the neuron of the layer
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