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.

在检测冠状病毒的医学影像程序中，除了医学检测之外，诊断的批准也具有特殊的意义。成像程序对于检测 COVID-19 造成的损害也很有用。胸部 X 射线成像经常用于诊断 COVID-19 和不同的肺炎。本文提出了一个用于在 X 射线图像中检测冠状病毒的特定任务框架。对两个不同的数据集进行了三种不同标签（健康、细菌性肺炎和 COVID-19）的二元分类，其中电晕被标记为阳性。分析一阶统计量、灰度共生矩阵、灰度游程矩阵和灰度大小区域矩阵，形成十五个子数据集并确定必要的放射组学。比较两种标准化方法以使数据有意义。此外，还提到了五种特征排序方法（ Bhattacharyya 、 entropy 、 Roc 、 t-test和Wilcoxon ），为基于高斯图的混沌粒子群优化和神经网络的最先进的分类器提供必要的信息。 。所提出的框架是根据放射组学、归一化方法和基于过滤器的特征排序方法的分析而设计的。在实验中，评估了七个指标来客观地确定结果：准确性、受试者工作特征 (ROC) 曲线下面积、灵敏度、特异性、g 均值、精度和 f 测量。所提出的框架在两个基于 X 射线的数据集上显示出有希望的分数，特别是对于冠状病毒与.其他的。