Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.

中文翻译：

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COVID-19：一种用于分类的新型深度学习计算机辅助模型

胸部X射线（CXR）成像是早期发现COVID-19病毒感染的最可行诊断方式之一，根据世界卫生组织（WHO）2019年12月的报告，该疾病被列为大流行。COVID- 19是属于冠状病毒家族的快速自然共有病毒。CXR扫描是早期发现COVID-19以便进一步监控并控制其病毒传播的重要工具之一。COVID-19的分类旨在检测受试者是否被感染。在本文中，基于三个不同的COVID-19 X射线数据集，提出了一个用于分析和评估灰度CXR图像的模型，称为胸部X射线COVID网络（CXRVN）。提出的CXRVN模型是一种轻量级体系结构，它依赖于表示基本功能的单个完全连接的层，因此与预训练的模型相比，它减少了总内存使用量和处理时间。CXRVN采用两个优化器：小批量梯度下降和Adam优化器，该模型的性能几乎相同。此外，CXRVN接受灰度的CXR图像，这是CXR的理想图像表示形式，并且消耗更少的内存存储和处理时间。因此，CXRVN可以在几毫秒内高精度分析CXR图像。学习过程的结果集中在使用称为SoftMax的评分功能进行决策上，该评分功能可导致高比率的真实-阳性分类。使用三个不同的数据集来训练CXRVN模型，并将其与预先训练的模型进行比较：GoogleNet，ResNet和AlexNet，在评估过程中使用了微调和转移学习技术。为了验证CXRVN模型的有效性，我们根据众所周知的性能指标（例如精度，灵敏度，F1得分和准确性）对它进行了评估。基于灵敏度，精度，召回率，准确性和F1分数的评估结果表明，GAN增强后，两个类别的实验2（数据集2）的准确度达到96.7％，实验3（数据集3）的准确度达到93.07％ ）的三个类别，而所提出的CXRVN模型的平均准确度为94.5％。F1得分和准确性。基于灵敏度，精度，召回率，准确性和F1分数的评估结果表明，GAN增强后，两个类别的实验2（数据集2）的准确度达到96.7％，实验3（数据集3）的准确度达到93.07％ ）的三个类别，而所提出的CXRVN模型的平均准确度为94.5％。F1得分和准确性。基于灵敏度，精度，召回率，准确性和F1分数的评估结果表明，GAN增强后，两个类别的实验2（数据集2）的准确度达到96.7％，实验3（数据集3）的准确度达到93.07％ ）的三个类别，而所提出的CXRVN模型的平均准确度为94.5％。