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Intravascular ultrasound image plaque recognition based on improved Resnet network

Accurate analysis of plaque types in blood vessels can not only prevent the occurrence of cardiovascular diseases, but also provide a reliable basis for the treatment of diseases. Therefore, in view of the low accuracy of plaque recognition methods in intravascular ultrasound images and the need to extract features manually, this paper proposes a method to automatically extract image features and recognize plaques. The results show that on the same dataset, the accuracy rates of the Support Vector Machine (SVM) model and our method are 75.3% and 87.1%, respectively, and our method achieves higher recognition accuracy. In addition, we analyze the impact of different network structures on the recognition performance. The results show that the proposed method can not only improve the recognition accuracy, but also have simple structure and better real-time performance.