A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2023-02-01 DOI:10.1080/0954898X.2022.2147231

Saravanan Suba, M Muthulakshmi

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引用次数: 0

Abstract

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

查看原文本刊更多论文

系统综述:基于人工智能技术的胸片图像(x线图像)分析与COVID-19分类诊断。

由于严重急性呼吸系统综合征冠状病毒1(SARS - Co-V-2)， COVID-19大流行在各国造成了混乱。COVID-19症状的严重程度从感冒、呼吸问题、呼吸系统问题开始，这些问题也可能导致危及生命的情况。这种疾病具有广泛的传染性，并在人与人之间传播。当眼睛、鼻子和嘴巴等人体器官接触到被污染的液体时，污染就会扩散。这种病毒可以通过进行鼻咽拭子试验来筛查，这是耗时的。因此，医生们更喜欢快速检测方法，如胸部x线摄影图像和CT扫描。有时，从胸片图像中找到准确的疾病可能会发生一些混乱。为了克服这一问题，本研究回顾了几种用于胸部x射线图像的深度学习和机器学习程序。这也有助于专业人员发现除COVID-19之外胸部发生的其他类型的故障。这篇综述可以作为医生和放射科医生识别COVID-19和其他类型的人体解剖疾病病毒的指南，并可以很快提供援助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.