Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.
Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.
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DOI：
10.3390/ijerph18063056
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发表时间：
2021-03-16
影响因子：
--
通讯作者：
Althobiani F
中科院分区：
文献类型：
--
作者：
Irfan M;Iftikhar MA;Yasin S;Draz U;Ali T;Hussain S;Bukhari S;Alwadie AS;Rahman S;Glowacz A;Althobiani F
随着 2020 年的到来，COVID-19 综合症在全球范围内广泛升级，并导致数百万人患病。一旦症状恶化，COVID-19 患者的风险就会升高。因此，早期识别患病患者可以促进早期干预并避免疾病继发。本文打算开发一种混合深度神经网络 (HDNN)，利用计算机断层扫描 (CT) 和 X 射线成像来预测 COVID-19 患者发病的风险。准确地说，受试者被分为三类，即正常、肺炎和COVID-19。最初，被称为“混合图像”（分辨率为 1080 × 1080）的 CT 和胸部 X 射线图像是从不同来源收集的，包括 GitHub、COVID-19 放射摄影数据库、Kaggle、COVID-19 图像数据集和 Actual Med COVID-19 胸部 X 射线数据集，这些都是开源和公开的数据存储库。 80% 的混合图像用于训练混合深度神经网络模型，其余 20% 用于测试目的。 HDNN 的能力和预测精度是使用混淆矩阵计算的。混合深度神经网络在测试集数据上显示出 99% 的分类准确率。
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.
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影响因子：
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作者：
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通讯作者：
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DOI：
10.3390/e22121370
发表时间：
2020-12-04
期刊：
Entropy (Basel, Switzerland)
影响因子：
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作者：
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