The COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase-chain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used. Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.

中文翻译：

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使用微调的深度学习架构在胸部 X 射线图像中自动检测 COVID-19

COVID-19 大流行对全球人类健康产生了重大影响。这种疾病是由于存在一种病毒，这种病毒在一种广泛传播的疾病中表现出来，导致全世界的死亡率很高。根据研究，感染患者具有明显的影像学视觉特征，以及干咳、呼吸困难、发烧等症状。尽管逆转录聚合酶链反应 (RT-PCR) 测试已用于 COVID-19 测试，但其可靠性非常低。因此，计算机断层扫描和 X 射线图像已被广泛使用。人工智能与 X 射线技术相结合最近被证明在诊断这种疾病方面更有效。带着这个动机，已经对微调的深度学习架构进行了比较分析，以加快对来自其他肺炎组的 COVID-19 患者的检测和分类。用于此分析的模型是 MobileNetV2、ResNet50、InceptionV3、NASNetMobile、VGG16、Xception、InceptionResNetV2 DenseNet121，它们已使用一组新的层进行了微调，替换为网络头部。本研究工作对两个数据集进行了分析。Dataset-1 包括三类图像：正常、COVID 和肺炎。相比之下，Dataset-2 包含相同的类别，但更关注两个突出的肺炎类别：细菌性肺炎和病毒性肺炎。该研究对 959 幅 X 射线图像（细菌性肺炎 250 幅、病毒性肺炎 250 幅、COVID 209 幅和正常病例 250 幅）进行。使用混淆矩阵，已计算出不同模型所需的结果。对于第一个数据集，DenseNet121 获得了 97% 的准确率，而对于第二个数据集，MobileNetV2 以 81% 的准确率表现最好。