Roy Pradeep Kumar, Kumar Abhinav
Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, Gujarat, India.
Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India.
Comput Electr Eng. 2022 Jul;101:108018. doi: 10.1016/j.compeleceng.2022.108018. Epub 2022 Apr 28.
In the wake of the COVID-19 outbreak, automated disease detection has become a crucial part of medical science given the infectious nature of the coronavirus. This research aims to introduce a deep ensemble framework of transfer learning models for early prediction of COVID-19 from the respective chest X-ray images of the patients. The dataset used in this research was taken from the Kaggle repository having two classes-COVID-19 Positive and COVID-19 Negative. The proposed model achieved high accuracy on the test sample with minimum false positive prediction. It can assist doctors and technicians with early detection of COVID-19 infection. The patient's health can further be monitored remotely with the help of connected devices with the Internet, which may be termed as the Internet of Medical Things (IoMT). The proposed IoMT-based solution for the automatic detection of COVID-19 can be a significant step toward fighting the pandemic.
在新冠疫情爆发后，鉴于冠状病毒的传染性，自动化疾病检测已成为医学科学的关键部分。本研究旨在引入一个迁移学习模型的深度集成框架，用于从患者的胸部X光图像中早期预测新冠病毒。本研究使用的数据集来自Kaggle库，有两个类别——新冠病毒阳性和新冠病毒阴性。所提出的模型在测试样本上实现了高精度，且假阳性预测最少。它可以帮助医生和技术人员早期检测新冠病毒感染。借助与互联网连接的设备，患者的健康状况可以进一步远程监测，这可称为医疗物联网（IoMT）。所提出的基于IoMT的新冠病毒自动检测解决方案可能是抗击疫情的重要一步。
