Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers
Resource type
Authors/contributors
- Bernardo Gois, Francisco Nauber (Author)
- Lobo Marques, Joao Alexandre (Author)
- Fong, Simon James (Author)
- Lobo Marques, Joao Alexandre (Editor)
- Fong, Simon James (Editor)
Title
Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers
Abstract
COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.
Book Title
Computerized Systems for Diagnosis and Treatment of COVID-19
Place
Cham
Publisher
Springer International Publishing
Date
2023
Pages
79-97
Language
en
ISBN
978-3-031-30788-1
Accessed
10/10/23, 4:37 AM
Library Catalog
Springer Link
Extra
Citation
Bernardo Gois, F. N., Lobo Marques, J. A., & Fong, S. J. (2023). Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 79–97). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_6
Academic Units
United Nations SDGs
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