Deeplab V3+ based semantic segmentation of COVID-19 lesions in computed tomography images

Abstract EN

Coronavirus 2019 spreads rapidly worldwide causing a global epidemic.

early detection and diagnosis of COVID-19 is critical for treatment as it causes respiratory syndrome appears in the chest medical images, such as computed tomography (CT) images, and x-ray images.

the CT images are more sensitive and have more details compared to the x-ray images.

thus, automated segmentation plays an imperative role in detecting, diagnosing, and determining the spreading of COVID-19.

in this paper, the DeepLabV3 + combined with MobileNet-V2 model was implemented.

to validate this combination, we conducted a comparative study between the DeepLabV3 + variants by its combination with MobileNet-V2 against DeepLabV3 + combined with different CNN, namely ResNet-18, and ResNet50.

also, a comparative study with the basic traditional U-Net and modified Alex for segmentation was carried out.

the experimental results showed the superiority of the using DeepLabV3 + combined with MobileNet-V2 for COVID-19 segmentation by achieving 97.5% mean accuracy, 95.2% sensitivity, 99.7% specificity, 99.7% precision, 99.3 % weighted Jaccard coefficient, and 97.5% weighted dice coefficient.