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.

3t2FTS: A Novel Feature Transform Strategy to Classify 3D MRI Voxels and Its Application on HGG/LGG Classification

“…First-order statistics (FOS) are generated using the histogram-based intensity analyses of an image. Concerning the histogram evaluations, six phenomena (mean, standard deviation, skewness, kurtosis, energy, and entropy) constitute the most preferred FOS features in the literature [14][15][16][17].…”

“…Then, a discrete intensity value 'i' or the output of the function f (x,y) can own values in the range of [0, G − 1] which shows the values of intensity levels. Hereupon, the histogram arises as a statistical assessment of the repetition number of intensity levels among the image [14][15][16][17].…”

“…After this, the total voxel number of volume of interest (VOI) is obtained by multiplying the width 'W', length 'L', and slice number 'N'. In relation to this, h(i) and Kronecker delta function can be identified as in ( 1) and (2) [14][15][16][17].…”

Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

“…Another study proposes a novel hybrid classifier, using Gauss---map---based chaotic particle swarm optimization Neural network [40], which was designed to classify Coro--navirus, normal, and bacterial pneumonia X---ray images. The results showed 99.25% ac--curacy and 99.53% AUC metrics.…”

Radiomics’ application for diagnosis and treatment of COVID-19: A review

“…Rezaeijo et al7 based on the study of 9 feature selection procedures and 10 ML classifiers, it is shown that the ML model based on recursive feature elimination (RFE) and k-nearest neighborhood (KNN) classifier has the best performance in distinguishing confirmed and suspected patients with COVID-19 infection. When establishing the COVID-19 diagnosis model, we not only need to consider image quality, image sample quantity, and different data inputs, but also need to choose appropriate feature selection and prediction methods according to the actual situation to achieve the model optimization8 .…”