  Recently, the lung infection due to Coronavirus Disease (COVID-19) affected a
large human group worldwide and the assessment of the infection rate in the
lung is essential for treatment planning. This research aims to propose a
Machine-Learning-System (MLS) to detect the COVID-19 infection using the CT
scan Slices (CTS). This MLS implements a sequence of methods, such as
multi-thresholding, image separation using threshold filter,
feature-extraction, feature-selection, feature-fusion and classification. The
initial part implements the Chaotic-Bat-Algorithm and Kapur's Entropy (CBA+KE)
thresholding to enhance the CTS. The threshold filter separates the image into
two segments based on a chosen threshold 'Th'. The texture features of these
images are extracted, refined and selected using the chosen procedures.
Finally, a two-class classifier system is implemented to categorize the chosen
CTS (n=500 with a pixel dimension of 512x512x1) into normal/COVID-19 group. In
this work, the classifiers, such as Naive Bayes (NB), k-Nearest Neighbors
(KNN), Decision Tree (DT), Random Forest (RF) and Support Vector Machine with
linear kernel (SVM) are implemented and the classification task is performed
using various feature vectors. The experimental outcome of the SVM with
Fused-Feature-Vector (FFV) helped to attain a detection accuracy of 89.80%.
