Automatic Annotation Algorithm of Medical Radiological Images using Convolutional Neural Network

In order to address the problems of time-consuming, low accuracy and poor convergence effect of traditional image automatic annotation algorithm, an Automatic annotation algorithm of medical radiological images based on convolutional neural network (CNN) is proposed. First of all, the image gradient information model was constructed, the edge contour feature of medical radiation image was initialized, the automatic segmentation model of medical radiation image was established by block template matching method, and the automatic segmentation processing of medical radiation image was completed. Secondly, by fusing the contour and gray information of image segmentation, the multi-resolution feature is extracted by using the three-dimensional distributed pixel sequence of image. The fusion feature decomposition of the image was obtained based on CNN, and the automatic annotation of medical radiation image was completed. The results show that the image segmentation effect of the proposed algorithm is good, the number of feature points is accurate, and the accuracy of multi-resolution feature extraction is as high as 98.7%. The convergence of image annotation is good, short time-consumption, and the F1 measurement value of the algorithm is high, and the overall performance is good. (c) 2021 Elsevier B.V. All rights reserved.