Abstract
The first COVID-19 confirmed case is reported in Wuhan, China and spread
across the globe with unprecedented impact on humanity. Since this
pandemic requires pervasive diagnosis, it is significant to develop
smart, fast and efficient detection technique. To this end, we developed
an Artificial Intelligence (AI) engine to classify the lung inflammation
level (mild, progressive, severe stage) of the COVID-19 confirmed
patient. In particular, the developed model consists of two phases; in
the first phase, we calculate the volume and density of lesions and
opacities of the CT images of the confirmed COVID-19 patient using
Morphological approaches. In the second phase, the second phase
classifies the pneumonia level of the confirmed COVID-19 patient. To
achieve precise classification of lung inflammation, we use modified
Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The
result of the experiments show that the utilized models can provide the
accuracy up to 95.65\% and 91.304 \% of
CNN and kNN respectively.