Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine
Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine
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DOI：
10.33889/ijmems.2020.5.4.052
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发表时间：
2020-08-01
影响因子：
1.6
通讯作者：
Biswas, Preesat
中科院分区：
文献类型：
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作者：
Sethy, Prabira Kumar;Behera, Santi Kumari;Biswas, Preesat
冠状病毒（COVID-19）的检测现在是医生的一项关键任务。冠状病毒在人与人之间传播速度如此之快，全球已接近10万人。在这种情况下，识别受感染的人，以便采取预防传播的措施是非常必要的。在本文中，深度特征加支持向量机（SVM）为基础的方法，提出了冠状病毒感染的患者使用X射线图像检测。对于分类，使用SVM代替基于深度学习的分类器，因为后者需要大量数据集进行训练和验证。从CNN模型的全连接层中提取深层特征，并将其输入SVM进行分类。支持向量机分类电晕影响的X射线图像从其他。该方法包括三类X射线图像，即，COVID-19肺炎和正常。该方法有利于医生在COVID-19患者、肺炎患者和健康人之间进行分类。使用不同的13个CNN模型的深度特征评估SVM用于COVID-19的检测。SVM使用ResNet 50的深度功能产生了最好的结果。结果表明，该分类模型对COVID-19（忽略SARS、MERS和ARDS）的准确率、灵敏度、FPR和F1评分分别为95.33%、95.33%、2.33%和95.34%。同样，ResNet 50加SVM实现的最高准确率为98.66%。结果基于GitHub和Kaggle存储库中可用的X射线图像。由于数据集的规模为数百，基于SVM的分类比迁移学习方法更鲁棒。并与其他传统的分类方法进行了比较分析。传统的方法有局部二值模式（LBP）+支持向量机、梯度方向直方图（HOG）+支持向量机和灰度共生矩阵（GLCM）+支持向量机。在传统的图像分类方法中，LBP + SVM的分类准确率达到93.4%。
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.