A COVID‐19 CXR image recognition method based on MSA‐DDCovidNet

Currently, coronavirus disease 2019 (COVID‐19) has not been contained. It is a safe and effective way to detect infected persons in chest X‐ray (CXR) images based on deep learning methods. To solve the above problem, the dual‐path multi‐scale fusion (DMFF) module and dense dilated depth‐wise separab...

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Detalles Bibliográficos
Autores principales: Wang, Wei, Huang, Wendi, Wang, Xin, Zhang, Peng, Zhang, Nian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111165/
https://www.ncbi.nlm.nih.gov/pubmed/35601273
http://dx.doi.org/10.1049/ipr2.12474
Descripción
Sumario:Currently, coronavirus disease 2019 (COVID‐19) has not been contained. It is a safe and effective way to detect infected persons in chest X‐ray (CXR) images based on deep learning methods. To solve the above problem, the dual‐path multi‐scale fusion (DMFF) module and dense dilated depth‐wise separable (D3S) module are used to extract shallow and deep features, respectively. Based on these two modules and multi‐scale spatial attention (MSA) mechanism, a lightweight convolutional neural network model, MSA‐DDCovidNet, is designed. Experimental results show that the accuracy of the MSA‐DDCovidNet model on COVID‐19 CXR images is as high as 97.962%, In addition, the proposed MSA‐DDCovidNet has less computation complexity and fewer parameter numbers. Compared with other methods, MSA‐DDCovidNet can help diagnose COVID‐19 more quickly and accurately.

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