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A novel machine learning-based analytical framework for automatic detection of COVID-19 using chest X-ray images
International Journal of Imaging Systems and Technology ( IF 3.0 ) Pub Date : 2021-06-11 , DOI: 10.1002/ima.22613
Shikhar Johri 1 , Mehendi Goyal 2, 3 , Sahil Jain 3, 4 , Manoj Baranwal 3 , Vinay Kumar 5 , Rahul Upadhyay 5
Affiliation  

Considering the prevailing scenario of COVID-19 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare system. Currently, the most common and reliable testing method available for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (rRT-PCR) test. However, the chest radiological (X-ray) imaging can be used as an alternate method to rRT-PCR test, and early COVID-19 symptoms can be investigated by critical examination of patient's chest scans. In the present work, a novel machine learning (ML)-based analytical framework is developed for automatic detection of COVID-19 using chest X-ray (CXR) images of plausible patients. The framework is designed, trained, and validated to identify four classes of CXR images namely, healthy, bacterial pneumonia, viral pneumonia, and COVID-19. The experimental results pose the proposed framework as a potential candidate for COVID-19 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four-class classification. The comparative analysis demonstrates the better capabilities of the proposed framework COVID-19 detection along with other types of pneumonia.

中文翻译:

一种基于机器学习的新型分析框架,用于使用胸部 X 射线图像自动检测 COVID-19

考虑到 COVID-19 大流行的普遍情况,早期发现该疾病是疾病管理中重要且关键的一步。早期发现和正确治疗可以限制疾病进展到严重程度并防止死亡。此外,早期隔离感染患者将控制传播率,并可能减轻当前医疗保健系统的压力。目前,可用于 COVID-19 诊断的最常见和最可靠的测试方法是实时逆转录聚合酶链反应 (rRT-PCR) 测试。但是,胸部放射(X 射线)成像可用作 rRT-PCR 测试的替代方法,并且可以通过对患者的胸部扫描进行严格检查来调查早期 COVID-19 症状。在目前的工作中,开发了一种基于机器学习 (ML) 的新型分析框架,用于使用可信患者的胸部 X 射线 (CXR) 图像自动检测 COVID-19。该框架经过设计、训练和验证,可以识别四类 CXR 图像,即健康、细菌性肺炎、病毒性肺炎和 COVID-19。实验结果将所提出的框架作为使用 CXR 图像进行 COVID-19 疾病诊断的潜在候选者,在四类分类中的训练、验证和测试准确度分别为 92.4%、88.24% 和 87.13%。比较分析表明,提议的框架 COVID-19 检测以及其他类型的肺炎具有更好的能力。并经验证可识别四类 CXR 图像,即健康、细菌性肺炎、病毒性肺炎和 COVID-19。实验结果将所提出的框架作为使用 CXR 图像进行 COVID-19 疾病诊断的潜在候选者,在四类分类中的训练、验证和测试准确度分别为 92.4%、88.24% 和 87.13%。比较分析表明,提议的框架 COVID-19 检测以及其他类型的肺炎具有更好的能力。并经验证可识别四类 CXR 图像,即健康、细菌性肺炎、病毒性肺炎和 COVID-19。实验结果将所提出的框架作为使用 CXR 图像进行 COVID-19 疾病诊断的潜在候选者,在四类分类中的训练、验证和测试准确度分别为 92.4%、88.24% 和 87.13%。比较分析表明,提议的框架 COVID-19 检测以及其他类型的肺炎具有更好的能力。在四级分类中。比较分析表明,提议的框架 COVID-19 检测以及其他类型的肺炎具有更好的能力。在四级分类中。比较分析表明,提议的框架 COVID-19 检测以及其他类型的肺炎具有更好的能力。
更新日期:2021-08-05
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