A Deep Learning based System for Covid-19 Positive Cases Detection Using Chest X-ray Images

Thai Nguyen, Trong-Hop Do, Pham Quang Dung
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引用次数: 3

Abstract

Recent technology advancements open the door for the employment of deep learning-based methods in practically all spheres of human endeavor. Deep learning algorithms can be employed in the medical industry for the categorization and identification of various diseases because of their accuracy. The recent coronavirus (COVID-19) pandemic has significantly strained the global health system. By using medical imaging and PCR testing, COVID-19 can be diagnosed. Since COVID-19 is very communicable, chest X-ray diagnosis is frequently regarded as safe. In this report, a deep learning-based method is suggested for differentiating COVID-19 infections from other illnesses that aren't COVID-19. A pre-trained model, Densenet121 is employed to categorize COVID-19.
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基于深度学习的胸部x线图像检测Covid-19阳性病例系统
最近的技术进步为在人类努力的几乎所有领域使用基于深度学习的方法打开了大门。深度学习算法由于其准确性,可以应用于医疗行业对各种疾病进行分类和识别。最近的冠状病毒(COVID-19)大流行使全球卫生系统严重紧张。通过医学成像和PCR检测,可以诊断COVID-19。由于COVID-19具有很强的传染性,胸部x线诊断通常被认为是安全的。在这份报告中,提出了一种基于深度学习的方法来区分COVID-19感染与其他非COVID-19疾病。采用预训练模型Densenet121对COVID-19进行分类。
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