  Since the breakout of coronavirus disease (COVID-19), the computer-aided
diagnosis has become a necessity to prevent the spread of the virus. Detecting
COVID-19 at an early stage is essential to reduce the mortality risk of the
patients. In this study, a cascaded system is proposed to segment the lung,
detect, localize, and quantify COVID-19 infections from computed tomography
(CT) images Furthermore, the system classifies the severity of COVID-19 as
mild, moderate, severe, or critical based on the percentage of infected lungs.
An extensive set of experiments were performed using state-of-the-art deep
Encoder-Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature
Pyramid Network (FPN), with different backbone (encoder) structures using the
variants of DenseNet and ResNet. The conducted experiments showed the best
performance for lung region segmentation with Dice Similarity Coefficient (DSC)
of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with
the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant
performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of
91.85% using the FPN model with the DenseNet201 encoder. The achieved
performance is significantly superior to previous methods for COVID-19 lesion
localization. Besides, the proposed system can reliably localize infection of
various shapes and sizes, especially small infection regions, which are rarely
considered in recent studies. Moreover, the proposed system achieved high
COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity.
Finally, the system was able to discriminate between different severity levels
of COVID-19 infection over a dataset of 1,110 subjects with sensitivity values
of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical
infections, respectively.
