AVNC: Attention-Based VGG-Style Network for COVID-19 Diagnosis by CBAM

Abstract

To detect COVID-19 patients more accurately and more precisely, we proposed a novel artificial intelligence model. (Methods) We used previously proposed chest CT dataset containing four categories: COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy subjects. First, we proposed a novel VGG-style base network (VSBN) as backbone network. Second, convolutional block attention module (CBAM) was introduced as attention module into our VSBN. Third, an improved multiple-way data augmentation method was used to resist overfitting of our AI model. In all, our model was dubbed as a 12-layer attention-based VGG-style network for COVID-19 (AVNC) (Results) This proposed AVNC achieved the sensitivity/precision/F1 per class all above 95%. Particularly, AVNC yielded a micro-averaged F1 score of 96.87%, which is higher than 11 state-of-the-art approaches.(Conclusion) This proposed AVNC is effective in recognizing COVID-19 diseases.

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

“…The similarities between the proposed VIN and VGG-16 are itemized in Table 4. Apart from those six similarity aspects (Fernandes, 2021), there are several differences between the proposed VIN and VGG-16. The input of VGG-16 is 224 × 224 × 3, while the input of VIN is 176 × 176 × 1.…”

“…If the two operands are not with the same dimension, then the values are transmitted (copied) in such tactics that the spatial attentional values are transmitted by the channel dimension, and the channel attention values are transmitted by the spatial dimension (Fernandes, 2021).…”

Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

“…In future work, we plan to investigate the changes of brain structural connectivity in patients with eMCI and lMCI [50,51], which will improve our understanding of dynamic brain connectivity. Furthermore, relevant methods will provide enlightenment for an explainable diagnosis of cognitive impairment caused by COVID-19 [52][53][54].…”

AIoT Used for COVID-19 Pandemic Prevention and Control

“…However, in the context of the outbreak of novel coronavirus, the use of CT scans and X-rays may pose risks to suspected patients and doctors, due to cross-infection. On the other hand, only reading a large number of image scans and manually drawing the outline of lung lesions will delay COVID-19 diagnosis [ 13 ]. Therefore, it is of great significance to develop an intelligent diagnostic system based on the IoT system, which can assist front-line doctors in jointly fighting against COVID-19.…”