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CovMnet–Deep Learning Model for classifying Coronavirus (COVID-19).
- Source :
- Health & Technology; Sep2022, Vol. 12 Issue 5, p1009-1024, 16p
- Publication Year :
- 2022
-
Abstract
- Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21907188
- Volume :
- 12
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Health & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 159000043
- Full Text :
- https://doi.org/10.1007/s12553-022-00688-1