The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner The coronavirus spread so quickly between people and approaches 100,000 people worldwide In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose The SVM classifies the corona affected X-ray images from others The methodology consists of three categories of Xray images, i e , COVID-19, pneumonia and normal The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models The SVM produced the best results using the deep feature of ResNet50 The classification model, i e ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95 33%,95 33%,2 33% and 95 34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS) Again, the highest accuracy achieved by ResNet50 plus SVM is 98 66% The result is based on the Xray images available in the repository of GitHub and Kaggle As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach Also, a comparison analysis of other traditional classification method is carried out The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM In traditional image classification method, LBP plus SVM achieved 93 4% of accuracy

中文翻译：

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基于深度特征和支持向量机的冠状病毒病（COVID-19）检测

冠状病毒（COVID-19）的检测现在是执业医师的一项重要任务。冠状病毒如此迅速地在人与人之间传播，并在全球范围内接近100,000人。因此，识别感染者非常重要，以便能够防止传播在本文中，建议使用基于深度特征加支持向量机（SVM）的方法通过X射线图像检测冠状病毒感染的患者。为了进行分类，使用SVM代替基于深度学习的分类器，因为后者需要用于训练和验证的大型数据集提取了CNN模型的全连接层的深层特征并将其馈入SVM以进行分类。SVM对受其他电晕影响的X射线图像进行分类。该方法包括三类X射线图像，即COVID-19，肺炎和正常人该方法有利于医学从业者在COVID-19患者，肺炎患者和健康人群中进行分类。使用13种不同的CNN模型的深层特征，对SVM进行了COVID-19检测的评估。利用ResNet50的深层功能，SVM产生了最佳结果分类模型，即ResNet50和SVM，在检测COVID-时准确度，灵敏度，FPR和F1得分分别为95 33％，95 33％，2 33％和95 34％。 19（忽略SARS，MERS和ARDS）再次，ResNet50和SVM达到的最高准确度是98 66％。结果基于GitHub和Kaggle信息库中的Xray图像。由于数据集成百上千，因此基于分类与转移学习方法相比，SVM上的功能更强大对其他传统分类方法进行了比较分析。传统方法是局部二进制模式（LBP）加SVM，定向梯度直方图（HOG）加SVM和灰度共生矩阵（GLCM）加SVM。 ，LBP和SVM达到93 4％的精度