dc.contributor.author |
Dadhirao, Chandrika |
|
dc.contributor.author |
Rasool Reddy, K. |
|
dc.contributor.author |
Prasad Reddy Sadi, Ram |
|
dc.contributor.author |
Kumar Batchu, Raj |
|
dc.contributor.author |
Naga Prakash, K. |
|
dc.contributor.author |
Kumar Vuddagiri, Ravi |
|
dc.date.accessioned |
2024-04-26T12:58:42Z |
|
dc.date.available |
2024-04-26T12:58:42Z |
|
dc.date.issued |
2024-04-26 |
|
dc.identifier.issn |
2210-142X |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5620 |
|
dc.description.abstract |
The COVID-19 pandemic has had a devastating impact on global health, economies, and societies. Early detection
of COVID-19 is crucial to prevent transmission and reduce mortality, but conventional imaging techniques such as X-rays and
computed tomography (CT) scans have limitations in accessibility, cost, and sterilization. Therefore, in this study, we explore the
use of lung ultrasound (LUS) for COVID-19 diagnosis and evaluate the performance of various deep learning (DL) models such
as AlexNet, ResNet, DenseNet, Inception, VGG, Inception-ResNet, MobileNet, and Xception with transfer learning. In the
presented study, initially, we collected 455 COVID-19 and 226 non-COVID images, including bacterial pneumonia and healthy
subjects, from a POCOVID-Net database. However, DL networks demand more data to explore and develop a significant model,
so we employ data augmentation using geometric transformations. After that, we utilize the suggested deep transfer learning
architectures for identifying COVID-19 subjects from LUS images. Finally, we estimate the performance of these models by wellreceived
metrics such as sensitivity, specificity, precision, F1-score, the area under the curve (AUC), and accuracy. From the
experimental results, we observed that DenseNet-201 achieved 100% accuracy and outperformed other models. This indicates that
deep learning with transfer learning is a promising approach for COVID-19 identification from LUS data when data is scarce.
These findings could have important implications for improving the efficiency and accessibility of COVID-19 diagnosis,
particularly in resource-limited settings. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
COVID-19, DL, data augmentation, transfer learning, CT, X-ray, and LUS imagery. |
en_US |
dc.title |
Identification of COVID-19 from Lung Ultrasound (LUS) images using deep transfer learning - Experimental study |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/XXXXXX |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1 |
en_US |
dc.pageend |
23 |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authoraffiliation |
Department of CSE, Gandhi Institute of Technology and Management, Deemed to be University |
en_US |
dc.contributor.authoraffiliation |
Department of ECE, NRI Institute of Technology (Autonomous) |
en_US |
dc.contributor.authoraffiliation |
Department of CSE, Gandhi Institute of Technology and Management, Deemed to be University & Department of IT, Anil Neerukonda Institute of Technology and Sciences |
en_US |
dc.contributor.authoraffiliation |
Department of CSE, School of Computing Amrita Vishwa Vidyapeetham |
en_US |
dc.contributor.authoraffiliation |
Department of ECE, Seshadri Rao Gudlavalleru Engineering College (SRGEC) |
en_US |
dc.contributor.authoraffiliation |
Department of ECE, Koneru Lakshmaiah Educational Foundation |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |