Department of Computer Science, University of Calgary, Calgary, AB, Canada.
Department of Computer Engineering, Ankara Medipol University, Ankara, Turkey.
Med Biol Eng Comput. 2023 Jun;61(6):1257-1297. doi: 10.1007/s11517-022-02758-y. Epub 2023 Jan 28.
The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.
由 SARS-CoV-2 病毒引起的持续的 COVID-19 大流行已经导致 660 万人死亡，超过 6.37 亿人感染，自 2019 年 12 月首次出现该疾病以来仅 30 个月。因此，快速准确地检测和诊断疾病是全世界的首要任务。研究人员一直在研究 COVID-19 的各种检测方法，由于该疾病感染肺部，因此肺部图像分析已成为用于检测疾病存在的热门研究领域。胸部 X 光（CXR）、计算机断层扫描（CT）图像和肺部超声图像的医学图像已被人工智能（AI）和机器学习（ML）基于方法的自动图像分析系统使用。各种现有的和新颖的 ML、深度学习（DL）、迁移学习（TL）和混合模型已被应用于检测和分类 COVID-19、感染区域的分割、评估严重程度以及从 COVID-19 患者的医学图像跟踪患者的进展。在本文中，对基于 COVID-19 的图像分析的一些最新方法进行了全面回顾，调查了现有研究工作的贡献、可用的图像数据集以及最近作品中使用的性能指标。还讨论了从 AI 角度应对 COVID-19 斗争进展的挑战和未来研究范围。因此，本文的主要目的是通过分析其新颖性和效率，提供使用 ML、DL 和 TL 模型从医学图像数据集中进行 COVID 检测和分析的研究工作的摘要，同时提到其他基于 COVID-19 的综述/调查研究，以提供有关 COVID-19 现有研究的最大信息量的简要概述。
