Abstract
COVID-19 has been identified as a highly contagious and rapidly spreading disease around the world. The high infection and mortality rate characterizes this as a very dangerous disease and has been marked as a global pandemic by the world health organization. Existing COVID-19 testing methods, such as RT-PCR are not completely reliable or convenient. Since the virus affects the respiratory tract, manual analysis of chest X-rays could be a more reliable but not convenient or scalable testing technique. Hence, there is an urgent need for a faster, cheaper, and automated way of detecting the presence of the virus by automatically analyzing chest X-ray images using deep learning algorithms. ResNetV2 is one of the pre-trained deep convolutional neural network models that could be explored for this task. This paper aims to utilize the ResNetV2 model for the detection of COVID-19 from chest X-ray images to maximize the performance of this task. This study performs fine-tuning of ResNetV2 networks (specifically, ResNet101V2), which is performed in two main stages: firstly, training model with frozen ResNetV2 base layers, and secondly, unfreezing some layers of the ResNetV2 and retraining with a lower learning rate. Model fine-tuned on ResNet101V2 shows competitive and promising results with 98.50% accuracy and 97.24% sensitivity.
Original language | English |
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Title of host publication | Intelligent Computing |
Subtitle of host publication | Proceedings of the 2022 Computing Conference |
Editors | Kohei Arai |
Place of Publication | Cham |
Publisher | Springer |
Pages | 106-116 |
Number of pages | 11 |
Volume | 2 |
ISBN (Electronic) | 9783031104640 |
ISBN (Print) | 9783031104633 |
DOIs | |
Publication status | Published - 7 Jul 2022 |
Externally published | Yes |
Event | Computing Conference 2022 - Virtual, United Kingdom Duration: 14 Jul 2022 → 15 Jul 2022 https://saiconference.com/Conferences/Computing2022 |
Publication series
Name | Lecture Notes in Networks and Systems |
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Volume | 507 LNNS |
ISSN (Print) | 2367-3370 |
ISSN (Electronic) | 2367-3389 |
Conference
Conference | Computing Conference 2022 |
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Country/Territory | United Kingdom |
Period | 14/07/22 → 15/07/22 |
Internet address |
Keywords
- Convolutional neural networks
- Covid19
- ResNet
- Fine-tuning