DBM-ViT: A multiscale features fusion algorithm for health status detection in CXR / CT lungs images

Abstract

COVID-19 is a severe acute respiratory syndrome caused by SARS-CoV-2. It is highly contagious and spreads rapidly around the world. Although reverse transcription-polymerase chain reaction (RT-PCR) of viral nucleic acid is standard practice in confirmed patients. However, in the early stage of infection, RT-PCR has the disadvantages of low detection sensitivity and long detection cycles. Chest X-ray (CXR) and lung computed tomography (CT) scans combined with intelligent image recognition have the advantages of fast detection speed and low price. However, the traditional convolutional neural network (CNN) has a small receptive field in the image recognition process, which is not conducive to capturing the global features of the image. A single vision transformer (ViT) model lacks the characteristics of inductive bias. These affect the extraction of image fusion features. This paper proposed a DBM-ViT model for deep learning. The model utilized CXR/CT lung images for effective health detection of normal, COVID-19 and other types of pneumonia. The model employed depthwise convolutions with different expansion rates to efficiently captured global information from CXR/CT lung images. Then, the lung feature maps with combined sequences were fed into the ViT module to capture local information. Multi-scale features combined with global and local information ensure maximum feature learning. The results show that the detection accuracy of the DBM-ViT model in the CXR/CT image dataset reached 97.25%/98.36%. This method can effectively capture global and local information in lung images with high detection accuracy and can be used for rapid auxiliary diagnosis of pneumonia types.