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. 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, 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.