Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class

TL;DR

This paper presents a machine-learning system that classifies lung CT scans into normal or COVID-19, achieving nearly 90% accuracy using feature fusion and SVM classifier.

Contribution

It introduces a novel pipeline combining thresholding, feature extraction, and fusion techniques with multiple classifiers, notably SVM, for COVID-19 detection from CT scans.

Findings

SVM with fused features achieved 89.80% accuracy.

The proposed method effectively distinguishes COVID-19 from normal scans.

Feature fusion improves classification performance.

Abstract

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%.