COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2020-08-18 eCollection Date: 2020-01-01 DOI:10.1155/2020/8828855
Mohd Zulfaezal Che Azemin, Radhiana Hassan, Mohd Izzuddin Mohd Tamrin, Mohd Adli Md Ali
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引用次数: 96

Abstract

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.

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使用公开可用的放射科医生评审的胸部x射线图像作为训练数据的COVID-19深度学习预测模型:初步发现。
深度学习研究的关键部分是训练数据集的可用性。由于公开可用的COVID-19胸部x线图像数量有限,基于这些图像开发的深度学习模型检测COVID-19病例的泛化和鲁棒性值得怀疑。我们的目标是使用数千张现成的具有与COVID-19相关临床表现的胸片图像作为训练数据集,与已确诊的COVID-19病例的图像相互排斥,这些图像将用作测试数据集。我们使用了一个基于ResNet-101卷积神经网络架构的深度学习模型,该模型经过预训练,可以从一百万张图像中识别物体,然后再进行重新训练,以检测胸部x射线图像中的异常。该模型在受试者工作曲线下面积、灵敏度、特异度和准确度方面的表现分别为0.82、77.3%、71.8%和71.9%。本研究的优势在于使用与COVID-19病例具有强烈临床相关性的标签,并使用相互排斥的公开数据进行培训、验证和测试。
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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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