Abstract

A new type of coronavirus called (SARS-CoV-2) causes the COVID-19 coronavirus disease. The World Health Organization (WHO) declared this COVID-19 disease as pandemic because the disease got spread over several countries. At present situation, there is no medicine available for prevention or cure of the infectious disease. Samples taken from persons with COVID-19 symptoms are commonly tested using Reverse Transcription–Polymerase Chain Reaction (RT-PCR) process which is costlier and also take a minimum of 24 h to get the test result as either negative or positive. The proposed work suggests the possibility of using X-ray images of persons having COVID-19 symptoms to be classified as 1) healthy, 2) COVID-19 affected, or 3) Pneumonia affected. Experimentation is carried out with data samples from each category and classification done using Convolutional Neural Network (CNN), transfer learning using VGG Net, and machine learning techniques such as Support Vector Machine (SVM) and XGBoost which utilizes features extracted with the help of Convolutional Neural Network. Out of the models compared, the SVM with CNN extracted features was able to produce a highest precision, recall, F1-score and accuracy of 95.27, 94.52, 94.94, and 95.81%, respectively in identifying healthy, Pneumonia, and COVID-19 affected persons while experimented with 5-fold cross validation.

中文翻译：

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使用卷积神经网络、迁移学习和使用深度特征的机器学习分类器对 COVID-19 X 射线图像分类的比较分析

摘要

一种称为 (SARS-CoV-2) 的新型冠状病毒会导致 COVID-19 冠状病毒病。世界卫生组织 (WHO) 宣布这种 COVID-19 疾病为大流行病，因为该疾病已蔓延到多个国家。目前，尚无预防或治疗传染病的药物。从有 COVID-19 症状的人身上采集的样本通常使用逆转录-聚合酶链反应 (RT-PCR) 过程进行测试，该过程成本更高，并且至少需要 24 小时才能获得测试结果为阴性或阳性。拟议的工作表明有可能使用具有 COVID-19 症状的人的 X 射线图像分类为 1) 健康，2) COVID-19 受影响，或 3) 肺炎受影响。对每个类别的数据样本进行实验，使用卷积神经网络 (CNN) 进行分类，使用 VGG 网络进行迁移学习，以及机器学习技术，如支持向量机 (SVM) 和 XGBoost，它们利用在卷积神经网络的帮助下提取的特征神经网络。在比较的模型中，具有 CNN 提取特征的 SVM 在识别健康、肺炎和 COVID-19 受影响时能够产生最高的准确率、召回率、F1 分数和准确率，分别为 95.27、94.52、94.94 和 95.81%人同时进行 5 折交叉验证。