COVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Novel coronavirus or SARS-COV-2 strain is responsible for COVID-19 and it has already shown the deadly nature of respiratory disease by threatening the health of millions of lives across the globe. Clinical study reveals that a COVID-19 infected person may experience dry cough, muscle pain, headache, fever, sore throat and mild to moderate respiratory illness. At the same time, it affects the lungs badly with virus infection. So, the lung can be a prominent internal organ to diagnose the gravity of COVID-19 infection using X-Ray and CT scan images of chest. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect coronavirus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting coronavirus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of coronavirus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation achieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.

中文翻译：

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使用多图像增强深度学习模型对胸部X射线和CT扫描图像进行COVID-19检测

直到最近，COVID-19仍被认为是极具传染性和致命性的肺炎型疾病。新型冠状病毒或SARS-COV-2毒株负责COVID-19，它已经威胁到全球数百万生命的健康，从而显示出呼吸系统疾病的致命性质。临床研究表明，感染COVID-19的人可能会出现干咳，肌肉疼痛，头痛，发烧，喉咙痛和轻度至中度呼吸道疾病。同时，病毒感染严重影响肺部。因此，使用X射线和胸部CT扫描图像，肺部可以成为诊断COVID-19感染严重程度的重要内脏器官。尽管测试时间很长，但RT-PCR是检测冠状病毒感染的可靠方法。有时，它可能会产生比预期比率更多的假阳性和假阴性结果。因此，为了帮助传统的RT-PCR方法进行准确的临床诊断，可以对个人肺部进行X射线和CT扫描，以进行COVID-19筛查。这种基于图像的诊断将在检测人体中的冠状病毒感染方面带来根本性的改变，轻松实现假阳性率和假阴性率零或接近零。本文报道了一种基于卷积神经网络（CNN）的多图像增强技术，用于检测可疑冠状病毒患者的胸部X射线和胸部CT扫描图像中的COVID-19。多图像增强利用在滤波后的图像中获得的不连续性信息来增加训练CNN模型的有效示例的数量。使用这种方法，所提出的模型显示出更高的分类精度，分别为95.38％和98。CT扫描和X射线图像分别占97％。具有多图像增强功能的CT扫描图像可达到94.78％的灵敏度和95.98％的特异性，而具有多图像增强功能的X射线图像可达到99.07％的灵敏度和98.88％的特异性。对包含胸部X射线和CT扫描图像的公开数据库进行了评估，并将实验结果与ResNet-50和VGG-16模型进行了比较。