Automatic classification of carotid ultrasound images based on convolutional neural network

Ultrasound imaging has become a routine means of diagnosing atherosclerosis. The classification of carotid ultrasound images and detection for the plaques automatically are critical for the diagnosis of atherosclerosis, which has important clinical significance for further analysis of plaque vulnerability and risk assessment of cardiovascular and cerebrovascular events. At present, manual measurement is used for the classification, which has obvious disadvantages such as inaccurate measurement and operator variability. In this paper, we proposed an automatic classification method based on convolutional neural network (CNN) for the carotid ultrasound images from different research institutions and ultrasound machines. 820 and 830 carotid ultrasound images from Zhongnan Hospital of Wuhan University and Robarts Research Institute of Canada were used for the classification of normal, thickened vessel wall and plaque images. To solve the problem of uneven image quality and size, we used six different image normalization schemes. Furthermore, we designed five CNNs with slightly different structures and compared them with texture-based features classifications. The CNN results showed significant superiority in classification performance with total accuracy of 90.30% and recall rate of 89.70%, indicating the automatic classification of carotid ultrasound images based on CNN is potentially useful for clinical application in the diagnosis of carotid atherosclerosis.