Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques.
Dis Markers
; 2021: 5522729, 2021.
Article
in English
| MEDLINE | ID: covidwho-1202046
ABSTRACT
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%.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Tomography, X-Ray Computed
/
Machine Learning
/
COVID-19
/
Lung
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Dis Markers
Journal subject:
Biochemistry
Year:
2021
Document Type:
Article
Affiliation country:
2021
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