Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.
Technol Health Care
; 30(6): 1273-1286, 2022.
Article
in English
| MEDLINE | ID: covidwho-2119015
ABSTRACT
BACKGROUND:
The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment.OBJECTIVE:
The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images.METHODS:
The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results.RESULT:
The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques.CONCLUSION:
The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Technol Health Care
Journal subject:
Biomedical Engineering
/
Health Services
Year:
2022
Document Type:
Article
Affiliation country:
THC-220114
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