Karthik R, Menaka R, Hariharan M, Kathiresan G S
Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.
School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India.
Ing Rech Biomed. 2022 Oct;43(5):486-510. doi: 10.1016/j.irbm.2021.07.002. Epub 2021 Jul 26.
In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.
The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.
The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.
This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
近年来，人工智能对研究应对不同领域挑战的方式产生了显著影响。事实证明，它是一项巨大的资产，尤其是在医学领域，能提供高效且可靠的解决方案。本研究旨在突出深度学习和机器学习模型在从医学图像中检测新冠病毒方面的影响。这是通过对该领域近期研究提出的最新方法进行综述来实现的。
本研究的主要重点是基于图像的新冠病毒检测中分类和分割方法的最新进展。该研究对在不同学术研究数据库中发表的140篇研究论文进行了综述。这些论文已根据特定标准进行筛选和过滤，以获取对基于图像的新冠病毒检测有价值的见解。
本综述中讨论的方法包括不同类型的成像模态，主要是X射线和CT扫描。这些模态也用于分类和分割任务。本综述旨在根据所使用的成像模态对用于这些任务的不同深度学习和机器学习架构进行分类和讨论。它还暗示了可以提出的其他可能的深度学习和机器学习架构，以期在新冠病毒检测方面取得更好的结果。与此同时，还讨论了基于人工智能的新冠病毒检测中新兴趋势和突破的详细概述。
这项工作最后阐述了研究人员面临的技术和非技术挑战，并说明了使用人工智能技术进行基于图像的新冠病毒检测的优势。
