Abstract Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, we proposed a novel intelligent computer vision method to automatically detect the Covid-19 virus. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called as Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN) and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV) and 10-fold cross-validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that we reached the perfect classification rate by using X-ray image for Covid-19 detection.

中文翻译：

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使用肺部 X 射线图像的基于自动残留样本局部二值模式和迭代 ReliefF 的电晕检测方法

摘要 冠状病毒通常是从动物传播到人，但现在它通过改变形态在人与人之间传播。Covid-19 是一种非常危险的病毒，不幸的是引起了世界范围内的大流行。放射科医生使用 X 射线或 CT 图像来诊断 Covid-19。使用图像处理方法帮助诊断此类图像变得至关重要。因此，我们提出了一种新颖的智能计算机视觉方法来自动检测Covid-19病毒。所提出的自动 Covid-19 检测方法由预处理、特征提取和特征选择阶段组成。预处理阶段使用图像调整大小和灰度转换。所提出的特征生成方法称为残差示例局部二进制模式（ResExLBP）。在特征选择阶段，使用了一种新颖的基于迭代ReliefF（IRF）的特征选择。分类阶段选择决策树（DT）、线性判别（LD）、支持向量机（SVM）、k 最近邻（kNN）和子空间判别（SD）方法作为分类器。留一交叉验证（LOOCV）和10折交叉验证用于训练和测试。在这项工作中，SVM 分类器通过使用 10 倍交叉验证实现了 100.0% 的分类准确率。这一结果清楚地表明，我们使用 X 射线图像进行 Covid-19 检测达到了完美的分类率。