Background Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, and , are investigated for semantically segmenting infected tissue regions in CT lung images. Methods We propose to use two known deep learning networks, and , for image tissue classification. is characterized as a scene segmentation network and as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly. Results The results show the superior ability of in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the shows better results as a multi-class segmentor (with 0.91 mean accuracy). Conclusion Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.

Background: Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and UNET, are investigated for semantically segmenting infected tissue regions in CT lung images.Methods: We propose to use two known deep learning networks, SegNet and UNET, for image tissue classification. SegNet is characterized as a scene segmentation network and UNET as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Severalstatistical scores are calculated for the results and tabulated accordingly.Results: The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the UNET shows better results as a multi-class segmentor (with 0.91 mean accuracy).Conclusion: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic wouldhelp automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.

Background: Currently, there is an urgent need for efficient tools to assess in the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and UNET, are investigated for semantically segmenting infected tissue regions in CT lung images. Methods: We propose to use two known deep learning networks, SegNet and UNET, for image tissue classification. SegNet is characterized as scene segmentation network and UNet as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, and as multi-class segmentors to learn the infection type on the lung. Each network is trained using 72 data images, validated on 10 images and tested against the left 18 images. Several statistical scores are calculated for the results and tabulated accordingly. Results: The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the UNET shows better results as a multi-class segmentor (with 0.91 mean accuracy). Conclusion: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis , but also help in quantifying the severity of the disease ,and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.

Background: Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lungimages of such patients. Two structurally-different deep learning techniques, SegNet and UNET, areinvestigated for semantically segmenting infected tissue regions in CT lung images.Methods: We propose to use two known deep learning networks, SegNet and UNET, for image tissueclassification. SegNet is characterized as as scene segmentation network and UNET as a medical segmentationtool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lungtissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained usingseventy-two data images, validated on ten images, and tested against the left eighteen images. Severalstatistical scores are calculated for the results and tabulated accordingly.Results: The results show the superior ability of SegNet in classifying infected/non-infected tissues comparedto the other methods (with 0:95 mean accuracy), while the UNET shows better results as a multi-classsegmentor (with 0:91 mean accuracy).Conclusion: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it wouldnot only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize thepopulation treatment accordingly. We propose computer-based techniques that prove to be reliable asdetectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic wouldhelp automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.

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