DA-COVSGNet: Double Attentional Network for COVID Severity Grading

COVID-19 is a respiratory disease that directly affects the lungs of infected individuals, leading to severe respiratory problems and lung infections. Although the severity of COVID-19 has decreased, the possibility of contracting the virus still exists, especially for individuals with underlying medical issues. Diagnosis of the severity of COVID-19 is very important in providing essential services to patients, improving treatment outcomes, and reducing complications and mortality rates associated with the virus. But, distinguishing of the severity of COVID-19 is a challenging task. COVID-19 is divided into four classes as far as its severity is concerned: normal-PCR+, mild, moderate, and severe. To overcome this challenge, we have introduced a novel method called DA-COVSGNet, based on a Convolutional Neural Network (CNN). In The proposed model, we preprocessed X-ray images using Synthetic Minority Over-Sampling Technique (SMOTE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) techniques, and fed them to the CNN architecture. Furthermore, we used new attention mechanisms to aid in better distinguishing and classifying disease severity levels, resulting in higher accuracy in classifying disease severity classes. Finally, we evaluated our proposed method on the COVIDGR dataset. The results show that our proposed method achieved accuracies of 96.7%, 96.2%, 98.5%, and 95.2% for the categories of Normal-PCR+, mild, moderate, and severe, respectively.

DA-COVSGNet: Double Attentional Network for COVID Severity Grading Keywords:

Chest x-ray images , COVID-19 , Contrast Limited Adaptive Histogram Equalization , Synthetic Minority Over-Sampling Technique , attention , Severity classification