COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach’s effectiveness.

COVID-19 是当今一种具有挑战性的全球性流行病，它以非常快的方式在人与人之间传播。有必要开发一种自动识别 COVID-19 的技术。这项工作研究了一种基于 X 射线图像预测 COVID-19 的新框架。建议的方法包含预处理、特征提取、选择和分类等核心阶段。引导和 2D 高斯滤波器作为预处理阶段用于图像改进。然后将结果传递给感兴趣区域 (ROI) 的二维超像素方法。然后应用 Darknet-53 和 Densenet-201 等预训练模型从分割图像中提取特征。熵编码的 GLEO 特征选择基于提取和选择的特征，并连续集成以产生单个特征向量。最终将单个向量作为输入提供给 SVM 分类器的变体，以对正常/异常 (COVID-19) X 射线图像进行分类。所提出的方法通过称为准确性、召回率、F1 分数和精度的不同度量进行评估。所提出系统的集成框架在 SVM 分类器上达到了可接受的精度，这验证了所提出方法的有效性。