使用卷积神经网络从x射线图像中自动检测COVID-19的迁移学习

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
Mundher Mohammed Taresh, N. Zhu, T. Ali, Asaad Shakir Hameed, Modhi Lafta Mutar
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引用次数: 72

摘要

新型冠状病毒肺炎(新冠肺炎)是一种传染性疾病,已导致全球数千人死亡,数百万人感染。因此,所有能够高精度快速检测COVID-19感染的技术设备都可以为医疗保健专业人员提供帮助。本研究旨在探索人工智能(AI)在基于胸部X射线成像的新冠肺炎快速可靠检测中的有效性。在这项研究中,应用可靠的预先训练的深度学习算法,实现了从数字胸部X射线图像中自动检测COVID-19诱导的肺炎。此外,该研究旨在评估近年来为医学图像分类提出的先进神经结构的性能。实验中使用的数据集涉及274例新冠肺炎病例、380例病毒性肺炎病例和380例健康病例,这些数据来源于几个开放的X射线来源和在线数据。混淆矩阵为测试后分类模型提供了基础。此外,还使用了一个开源的PYCM库来支持统计参数。该研究揭示了vgg16模型与用于进行这项研究的其他模型相比的优势,在这些模型中,该模型在总分和基于班级的分数方面表现最好。根据研究结果,X射线成像的深度学习可用于收集与新冠肺炎感染相关的关键生物标志物。该技术有助于医生对新冠肺炎感染进行诊断。同时,这种计算机辅助诊断工具的高精度可以显著提高新冠肺炎诊断的速度和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks
Novel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID- 19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images. Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was derived from several open sources of X-Rays, and the data available online. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PYCM was used to support the statistical parameters. The study revealed the superiority of Model vgg16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep Learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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