应用深度学习方法从胸部X射线中检测新冠肺炎的调查

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Bhargavinath Dornadula, S. Geetha, L. Anbarasi, Seifedine Kadry
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引用次数: 0

摘要

冠状病毒(新冠肺炎)的爆发为整个世界打开了一个令人担忧的局面,并被标记为过去一百年来最严重和最急性的医疗状况之一。包括计算机断层扫描(CT)和胸部x光片在内的各种医学成像模式被用于诊断。本文概述了最近开发的使用深度学习方法的胸部x射线图像新冠肺炎检测系统。这篇综述探讨和分析了文献中各种工作的数据集、特征工程技术、图像预处理方法和实验结果。它还强调了迁移学习技术和该领域研究人员使用的不同绩效指标。这些信息有助于指出未来新冠肺炎深度学习自动诊断领域的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey of COVID-19 Detection From Chest X-Rays Using Deep Learning Methods
The coronavirus (COVID-19) outbreak has opened an alarming situation for the whole world and has been marked as one of the most severe and acute medical conditions in the last hundred years. Various medical imaging modalities including computer tomography (CT) and chest x-rays are employed for diagnosis. This paper presents an overview of the recently developed COVID-19 detection systems from chest x-ray images using deep learning approaches. This review explores and analyses the data sets, feature engineering techniques, image pre-processing methods, and experimental results of various works carried out in the literature. It also highlights the transfer learning techniques and different performance metrics used by researchers in this field. This information is helpful to point out the future research direction in the domain of automatic diagnosis of COVID-19 using deep learning techniques.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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