使用混合学习技术通过计算机断层扫描图像预测新型冠状病毒肺炎

Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques.

作者信息

Perumal Varalakshmi, Narayanan Vasumathi, Rajasekar Sakthi Jaya Sundar

机构信息

Department of Computer Technology, MIT Campus, Anna University, Chennai, India.

Melmaruvathur Adhiparasakthi Institute of Medical Sciences and Research, Melmaruvathur, Chengalpattu District, India.

出版信息

Dis Markers. 2021 Apr 22;2021:5522729. doi: 10.1155/2021/5522729. eCollection 2021.

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%.

用于诊断新型冠状病毒肺炎（COVID-19）的逆转录聚合酶链反应（RT-PCR）在感染早期的检测率较低。与RT-PCR技术相比，CT图像的放射学分析具有更高的预测率。本文采用混合学习模型对COVID-19 CT图像、社区获得性肺炎（CAP）CT图像和正常CT图像进行分类，具有较高的特异性和敏感性。本文提出的系统与各种机器学习分类器和其他深度学习分类器进行了比较，以进行更好的数据分析。本研究的结果还与最近进行的其他关于COVID-19分类的研究进行了比较，以作进一步分析。结果发现，所提出的模型表现出色，准确率为96.69%，敏感性为96%，特异性为98%。