Diagnostic Model of Coronary Microvascular Disease Combined With Full Convolution Deep Network With Balanced Cross-Entropy Cost Function

Abstract

This paper addressed the vessel segmentation and disease diagnostic in coronary angiography image and proposed an Encoder-Decoder architecture of deep learning with End-to-End model, where Encoder is based on ResNet, and the deep features are exacted automatically, and the Decoder produces the segmentation result by balanced cross-entropy cost function. Furthermore, batch normalization is employed to decrease the gradient vanishing in the training process, so as to reduce the difficulty of training the deep neural network. The experiment results show that the algorithm effectively exacts the feature and edge information, therefore the complex background disturbance is suppressed convincingly, and the vessel segmentation precision is improved effectively, the segmentation precision for three typical vessels are 0.8365, 0.8924 and 0.6297 respectively; and the F-measure are 0.8514, 0.8786 and 0.7298, respectively. In addition, the experiment results show that our proposed can be generalized to the angiography image within limits.