A model fusion method based DAT-DenseNet for classification and diagnosis of aortic dissection.

In this paper, we proposed a complete study method to achieve accurate aortic dissection diagnosis at the patient level. Based on the CT angiography (CTA) images, a classification model named DAT-DenseNet, which combined the deep attention Transformer module with the DenseNet architecture is proposed. In the first phase, two DAT-DenseNet are combined in parallel. It is used to accurately achieve two classification task at the CTA images. In the second stage, we propose a feature fusion module. It concatenates and fuses the image features output from the two classification models on a patient by patient basis. In the comparison experiments of classification model performance, DAT-DenseNet obtained 92.41 $\%$ accuracy at the image level, which was 2.20 $\%$ higher than the commonly used model. In the comparison experiments of model fusion method, our method obtained 90.83 $\%$ accuracy at the patient level. The experiments showed that DAT-DenseNet model exhibits high performance at the image level. Our feature fusion module achieves the mapping from two classification image features to patient outcomes. It achieves accurate patient classification. The experiments' results in the Discussion section elaborate the details of the experiment and confirmed that the results were reliable.