OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19.
OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19.
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DOI：
10.1007/s10489-020-01904-z
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Chakrabartty DK
中科院分区：
文献类型：
--
作者：
Goel T;Murugan R;Mirjalili S;Chakrabartty DK
冠状病毒病（COVID-19）的快速传播已成为全球关注的问题，截至二零二零年七月，已影响超过1，500万名确诊患者。为了对抗这种扩散，临床成像，例如X射线图像，可以用于诊断。自动识别软件工具对于促进使用X射线图像筛查COVID-19至关重要。本文旨在从胸部X射线图像中对COVID-19、正常人和肺炎患者进行分类。因此，在这项工作中提出了一种优化的卷积神经网络（OptCoNet），用于COVID-19的自动诊断。建议的OptCoNet架构是由优化的特征提取和分类组件。灰狼优化器（GWO）算法用于优化训练CNN层的超参数。利用COVID-19、正常和肺炎图像的开放访问数据集，对所提出的模型进行了测试，并与不同的分类策略进行了比较。所提出的优化CNN模型的准确性、灵敏度、特异性、精确度和F1评分值分别为97.78%、97.75%、96.25%、92.88%和95.25%，优于最先进的模型。这种提出的CNN模型可以帮助自动筛查COVID-19患者，并减轻医疗服务框架的负担。
The quick spread of coronavirus disease (COVID-19) has become a global concern and affected more than 15 million confirmed patients as of July 2020. To combat this spread, clinical imaging, for example, X-ray images, can be utilized for diagnosis. Automatic identification software tools are essential to facilitate the screening of COVID-19 using X-ray images. This paper aims to classify COVID-19, normal, and pneumonia patients from chest X-ray images. As such, an Optimized Convolutional Neural network (OptCoNet) is proposed in this work for the automatic diagnosis of COVID-19. The proposed OptCoNet architecture is composed of optimized feature extraction and classification components. The Grey Wolf Optimizer (GWO) algorithm is used to optimize the hyperparameters for training the CNN layers. The proposed model is tested and compared with different classification strategies utilizing an openly accessible dataset of COVID-19, normal, and pneumonia images. The presented optimized CNN model provides accuracy, sensitivity, specificity, precision, and F1 score values of 97.78%, 97.75%, 96.25%, 92.88%, and 95.25%, respectively, which are better than those of state-of-the-art models. This proposed CNN model can help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.
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作者：
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通讯作者：
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DOI：
10.1148/ryct.2020200028
发表时间：
2020-02-01
期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
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