A New Pneumonia Detection Model Based on Transformer with Improved Self-Attention Mechanism

Abstract

Clinical diagnosis based on computed tomography (CT) could be used, as part of diagnosis standard of COVID-19 pneumonia. Addressing the problem that accuracy of CT-based traditional pneumonia classification diagnosis models is relatively low when employed for classification of community-acquired pneumonia (CP), COVID-19 pneumonia (NCP) and normal cases, a new network model is proposed which combines application of Swin Transformer and multi-head axial self-attention (MASA) mechanism, to analyze CT images and make intelligence-assisted diagnosis. The method in detail is to partially replace traditional multi-head self-attention (MSA) mechanism in encoders of Swin Transformer by MASA. The improved model is applied to train and test on commonly used pneumonia CT dataset CC-CCII. The results show that the proposed network outperforms traditional networks ResNet50 and Vision Transformer in indicators of accuracy, sensitivity and F1-measure.