从胸部x线图像诊断COVID-19感染肺部

T. R. Niloy, A. Rahman
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引用次数: 0

摘要

SARS-CoV-2是新发现的一种新型冠状病毒。病毒引起的不明原因的病因性肺炎,被称为2019冠状病毒病(COVID-19)。虽然这种疾病以一种新的方式出现,但没有药物可以治疗过境患者。因此,为了从x射线图像中诊断COVID-19感染的肺部,本文提出了一种自动化技术。在本研究中,使用卷积神经网络(CNN)和VGG19,准确率分别为97%和67%。对比分析表明,该方法的性能优于现有的求解方法。最终,Precision、Recall和F1-Score被提取出来,并在研究中解释了模型的损失函数。这项研究的重点是COVID-19的基本方面。因此,该技术可有效用于冠状病毒感染的诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnosis of COVID-19 Infected Lungs from Chest X-Ray Images
Severe Acute Respiratory Symptom Coronavirus 2 (SARS-CoV-2) was newly discovered as a beta coronavirus. The virus-induced unexplained etiological pneumonia and is referred to as the 2019 Coronavirus Disease (COVID-19). Though the disease has appeared in a new way, there is no medication for transited patients. So, for diagnosing the COVID-19 infected lungs from X-Ray images, an automated technique has been suggested in this manuscript. In this study, Convolutional neural network (CNN) and VGG19 were used and found accuracy scores of 97% and 67%, respectively. The comparative analysis shows that the proposed method performs better than the solution that exists. Eventually, Precision, Recall, and F1-Score have been extracted and interpreted the model's loss functions in the research. This research has carried out by focusing on essential aspects in terms of COVID-19. Therefore, for the diagnosis of coronavirus infection, the technique can be used effectively.
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