Automated detection of COVID-19 cases using deep neural networks with X-ray images
Automated detection of COVID-19 cases using deep neural networks with X-ray images
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DOI：
10.1016/j.compbiomed.2020.103792
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发表时间：
2020-06-01
影响因子：
7.7
通讯作者：
Acharya, U. Rajendra
中科院分区：
文献类型：
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作者：
Ozturk, Tulin;Talo, Muhammed;Acharya, U. Rajendra
2019年12月在中国武汉市首次出现的新型冠状病毒2019 （COVID-2019）在全球迅速蔓延，成为大流行。它对日常生活、公共卫生和全球经济都造成了毁灭性的影响。必须尽早发现阳性病例，以防止疫情进一步蔓延，并迅速治疗受影响的患者。由于没有准确的自动化工具包，对辅助诊断工具的需求增加了。利用放射成像技术获得的最新发现表明，此类图像包含有关COVID-19病毒的重要信息。应用先进的人工智能（AI）技术与放射成像相结合，有助于准确检测这种疾病，也有助于克服偏远村庄缺乏专业医生的问题。本研究提出了一种利用胸部x线图像自动检测COVID-19的新模型。该模型旨在为二元分类（COVID vs. No-Findings）和多类别分类（COVID vs. No-Findings vs.肺炎）提供准确的诊断。我们的模型对二分类的分类准确率为98.08%，对多分类的分类准确率为87.02%。在我们的研究中，暗网模型被用作你只看一次（YOLO）实时目标检测系统的分类器。我们实现了17个卷积层，并在每一层上引入了不同的滤波。我们的模型（可在（http://github.com/muhammedtalo/COVID-19）上获得）可用于协助放射科医生验证他们的初始筛查，也可通过云用于立即筛查患者。
The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (http://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.