COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier

Abstract

In medical imaging procedures for the detection of coronavirus, apart from medical tests, approval of diagnosis has special significance. Imaging procedures are also useful for detecting the damage caused by COVID-19. Chest X-ray imaging is frequently used to diagnose COVID-19 and different pneumonias. This paper presents a task-specific framework to detect coronavirus in X-ray images. Binary classification of three different labels (healthy, bacterial pneumonia, and COVID-19) was performed on two differentiated data sets in which corona is stated as positive. First-order statistics, gray level co-occurrence matrix, gray level run length matrix, and gray level size zone matrix were analyzed to form fifteen sub-data sets and to ascertain the necessary radiomics. Two normalization methods are compared to make the data meaningful. Furthermore, five feature ranking approaches (Bhattacharyya, entropy, Roc, t-test, and Wilcoxon) are mentioned to provide necessary information to a state-of-the-art classifier based on Gauss-map-based chaotic particle swarm optimization and neural networks. The proposed framework was designed according to the analyses about radiomics, normalization approaches, and filter-based feature ranking methods. In experiments, seven metrics were evaluated to objectively determine the results: accuracy, area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, g-mean, precision, and f-measure. The proposed framework showed promising scores on two X-ray-based data sets, especially with the accuracy and area under the ROC curve rates exceeding 99% for the classification of coronavirus vs. others.

Keywords: Binary categorization; Chaotic; Coronavirus; Framework design; Hybrid classifier; Optimization.

Graphical abstract

Fig. 1

Flowchart of GM-CPSO–NN.

Fig. 2

General scheme of framework analysis.

Fig. 3

Handicaps of data sets.

Fig. 4

Comparison of normalization approaches for data #1.

Fig. 5

Comparison of two frameworks for data #1.

Fig. 6

Comparison of two frameworks for data #2.

Fig. 7

Flowchart of the proposed framework.