CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease of the world, and the doctors need a lot of time to read the abdominal CT images for COPD pre-evaluation. To improve the pre-evaluation efficiency, a deep network based on slice correlation information was proposed for COPD auxiliary diagnosis. First, by using a grouping approach, the architecture of the deep network is divided into several network branches, each of which aims to extract the local intra-slice association information of the CT images. Then, the outputs from multiple network branches are integrated via a BiLSTM to extract the global inter-slice association information between the adjacent CT slices. To further improve the ability of local feature extraction for each network branch, the enhanced multi-headed convolutional attention is designed by embedding the ConvNeXt into the existing multi-headed convolutional attention. Experimental results show that the proposed deep network achieves promising effectiveness for CT image classification on auiliarily diagnose of COPD, and the accuracy, sensitivity, specificity and AUC of the proposed network reach to approximately 92.15%, 94.17%, 91.17% and 95.33%, respectively.