Panahi Amirhossein, Askari Moghadam Reza, Akrami Mohammadreza, Madani Kurosh
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
LISSI Lab, Senart-FB Institute of Technology, University Paris Est-Creteil (UPEC), Lieusaint, France.
SN Comput Sci. 2022;3(2):169. doi: 10.1007/s42979-022-01067-3. Epub 2022 Feb 21.
The COVID-19 diffused quickly throughout the world and converted as a pandemic. It has caused a destructive effect on both regular lives, common health and global business. It is crucial to identify positive patients as shortly as desirable to limit this epidemic's further diffusion and to manage immediately affected cases. The demand for quick assistant distinguishing devices has developed. Recent findings achieved utilizing radiology imaging systems propose that such images include salient data about the COVID-19. The utilization of progressive artificial intelligence (AI) methods linked by radiological imaging can help the reliable diagnosis of COVID-19. As radiography images can recognize pneumonia infections, this research brings an accurate and automatic technique based on a deep residual network to analyze chest X-ray images to monitor COVID-19 and diagnose verified patients. The physician states that it is significantly challenging to separate COVID-19 from common viral and bacterial pneumonia, while COVID-19 is additionally a variety of viruses. The proposed network is expanded to perform detailed diagnostics for two multi-class classification (COVID-19, Normal, Viral Pneumonia) and (COVID-19, Normal, Viral Pneumonia, Bacterial Pneumonia) and binary classification. By comparing the proposed network with the popular methods on public databases, the results show that the proposed algorithm can provide an accuracy of 92.1% in classifying multi-classes of COVID-19, normal, viral pneumonia, and bacterial pneumonia cases. It can be applied to support radiologists in verifying their first viewpoint.
新冠病毒在全球迅速传播并演变成一场大流行病。它对日常生活、公众健康和全球商业都造成了毁灭性影响。尽快识别出阳性患者对于限制这种流行病的进一步传播以及及时治疗受影响的病例至关重要。因此，对快速辅助诊断设备的需求应运而生。最近利用放射成像系统取得的研究结果表明，此类图像包含有关新冠病毒的重要数据。将先进的人工智能（AI）方法与放射成像相结合，有助于对新冠病毒进行可靠诊断。由于X光图像能够识别肺部感染情况，本研究提出了一种基于深度残差网络的准确且自动的技术，用于分析胸部X光图像，以监测新冠病毒并诊断确诊患者。医生指出，将新冠病毒与常见的病毒性和细菌性肺炎区分开来具有很大挑战性，因为新冠病毒本身也是多种病毒的一种。所提出的网络被扩展用于执行两种多类别分类（新冠病毒、正常、病毒性肺炎）和（新冠病毒、正常、病毒性肺炎、细菌性肺炎）以及二元分类的详细诊断。通过在公共数据库中将所提出的网络与常用方法进行比较，结果表明所提出的算法在对新冠病毒、正常、病毒性肺炎和细菌性肺炎病例进行多类别分类时，准确率可达92.1%。它可用于辅助放射科医生验证其初步诊断意见。
