使用放射学模式对Covid-19的检测和诊断进行深度学习：一份系统回顾（CS）

穆斯塔法·加德扎德 ·法尔霍恩德·阿萨迪

目的：早期发现和诊断Covid-19，以最低成本对非Covid-19病例的患者进行准确分离，这是Covid-19流行病的主要挑战之一。关于这种疾病的新颖性，基于放射性图像的诊断方法尽管在诊断中心有许多用途，但存在缺陷。因此，医学和计算机研究人员倾向于使用机器学习模型来分析放射学图像。 方法：从2019年11月1日至2020年7月20日，通过搜索PubMed、Scopus和科学网三个数据库进行系统回顾，根据搜索策略，关键词为Covid-19，深度学习、诊断和检测，最终抽取了168篇文章，通过应用包含和排除标准选出了37篇文章作为研究群体。

结果：本回顾研究概述了通过放射学模式检测和诊断Covid-19的所有模型的当前状态，以及基于深度学习的处理。根据该发现，基于深度学习的模型具有非凡的能力，能够实现一个准确和有效的系统，用于Covid-19的检测和诊断，这些系统用于CT扫描和X射线图像的处理，将导致灵敏度和特异性值的显著增加。 结论：深度学习（DL）在Covid-19放射图像处理领域的应用，可减少该病检测和诊断中的假阳性和阴性误差，为患者提供快速、廉价、安全的诊断服务提供了最佳机会。

Deep Learning in Detection and Diagnosis of Covid-19 using Radiology Modalities: A Systematic Review

Mustafa Ghaderzadeh, Farkhondeh Asadi

Purpose: Early detection and diagnosis of Covid-19 and accurate separation of patients with non-Covid-19 cases at the lowest cost and in the early stages of the disease are one of the main challenges in the epidemic of Covid-19. Concerning the novelty of the disease, the diagnostic methods based on radiological images suffer shortcomings despite their many uses in diagnostic centers. Accordingly, medical and computer researchers tended to use machine-learning models to analyze radiology images. Methods: Present systematic review was conducted by searching three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020 Based on a search strategy, the keywords were Covid-19, Deep learning, Diagnosis and Detection leading to the extraction of 168 articles that ultimately, 37 articles were selected as the research population by applying inclusion and exclusion criteria. Result: This review study provides an overview of the current state of all models for the detection and diagnosis of Covid-19 through radiology modalities and their processing based on deep learning. According to the finding, Deep learning Based models have an extraordinary capacity to achieve an accurate and efficient system for the detection and diagnosis of Covid-19, which using of them in the processing of CT-Scan and X-Ray images, would lead to a significant increase in sensitivity and specificity values. Conclusion: The Application of Deep Learning (DL) in the field of Covid-19 radiologic image processing leads to the reduction of false-positive and negative errors in the detection and diagnosis of this disease and provides an optimal opportunity to provide fast, cheap, and safe diagnostic services to patients.