A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19

Posted: 2021-09-11 19:00:00
Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on attention mechanism and depthwise separable convolution. A multi-scale gated multi-head attention mechanism is designed to extract effective feature information from the COVID-19 X-ray and CT images for classification. Moreover, the depthwise separable convolution layers are adopted as MGMADS-CNN's backbone to reduce the model size and parameters. The LeNet-5, AlexNet, GoogLeNet, ResNet, VGGNet-16, and three MGMADS-CNN models are trained, validated and tested with tenfold cross-validation on X-ray and CT images. The results show that MGMADS-CNN with three attention layers (MGMADS-3) has achieved accuracy of 96.75% on X-ray images and 98.25% on CT images. The specificity and sensitivity are 98.06% and 96.6% on X-ray images, and 98.17% and 98.05% on CT images. The size of MGMADS-3 model is only 43.6 M bytes. In addition, the detection speed of MGMADS-3 on X-ray images and CT images are 6.09 ms and 4.23 ms for per image, respectively. It is proved that the MGMADS-3 can detect and classify COVID-19 faster with higher accuracy and efficiency.

参考サイト PubMed: covid-19


6月 10, 2020 バイオアソシエイツ


最初は肺炎の形で肺に大きく影響すると考えられていた COVID-19 だが、2020年4月にCOVID-19に起因する多くの謎の症状の1つとして血栓が浮上した。 この直後、コロナウイルス関連の脳卒中が原因で若者が亡くなったという報告が出され、その次に、COVIDつま先という、痛みを伴う赤または紫の指が報告された。これらの症状のすべてに共通するものは何か? 血液循環の障害だ。…