Manipal Institute of Technology MAHE, Manipal, Karnataka 576104, India.
Department of IT, Mahaveer Institute of Science and Technology, Hyderabad, Telangana 500005, India.
Contrast Media Mol Imaging. 2022 Feb 25;2022:7377502. doi: 10.1155/2022/7377502. eCollection 2022.
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.
冠状病毒病（COVID-19）是由 SARS-CoV-2 引起的病毒性感染。计算机断层扫描（CT）等方式已成功用于 COVID-19 感染患者的早期诊断。最近，许多研究人员利用深度学习模型对 COVID-19 疑似病例进行自动筛查。提出了一种基于集成深度学习和物联网（IoT）的框架，用于筛查 COVID-19 疑似病例。集成了三个著名的预训练深度学习模型。利用医疗 IoT 设备来收集 CT 扫描，并在 IoT 服务器上进行自动诊断。将所提出的框架与四个类别的数据集上的十三个竞争模型进行了比较。实验结果表明，所提出的集成深度学习模型的准确率达到 98.98%。此外，该模型在其他性能指标方面优于所有竞争模型，达到 98.56%的精度、98.58%的召回率、98.75%的 F 分数和 98.57%的 AUC。因此，所提出的框架可以加快 COVID-19 的诊断速度。
