COVID-19 automatic diagnosis with CT images using the novel Transformer architecture
Resumo
Even though vaccines are already in use worldwide, the COVID-19 pandemic is far from over, with some countries re-establishing the lockdown state, the virus has taken over 2 million lives until today, being a serious health issue. Although real-time reverse transcription-polymerase chain reaction (RTPCR) is the first tool for COVID-19 diagnosis, its high false-negative rate and low sensitivity might delay accurate diagnosis. Therefore, fast COVID-19 diagnosis and quarantine, combined with effective vaccination plans, is crucial for the pandemic to be over as soon as possible. To that end, we propose an intelligent system to classify computed tomography (CT) of lung images between a normal, pneumonia caused by something other than the coronavirus or pneumonia caused by the coronavirus. This paper aims to evaluate a complete selfattention mechanism with a Transformer network to capture COVID-19 pattern over CT images. This approach has reached the state-of-the-art in multiple NLP problems and just recently is being applied for computer vision tasks. We combine vision transformer and performer (linear attention transformers), and also a modified vision transformer, reaching 96.00% accuracy.Referências
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Hasan, A.M.; AL-Jawad, M.M.; Jalab, H.A.; Shaiba, H.; Ibrahim, R.W.; AL-Shamasneh A.R.: Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features. Entropy 2020, 22, 517. https://doi.org/10.3390/e22050517
Aayush Jaiswal, Neha Gianchandani, Dilbag Singh, Vijay Kumar Manjit Kaur (2020) Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning, Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2020.1788642
Vaswani, Ashish, et al.: Attention is all you need. Advances in neural information processing systems. 2017. https://arxiv.org/abs/1706.03762
Dosovitskiy, Alexey, et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020). https://arxiv.org/abs/2010.11929
Choromanski, Krzysztof, et al.: Rethinking attention with performers. arXiv preprint arXiv:2009.14794 (2020). https://arxiv.org/abs/2009.14794
Gunraj, Hayden, LindaWang, and AlexanderWong.: Covidnet-ct: A tailored deep convolutional neural network design for detection of covid-19 cases from chest ct images. Frontiers in Medicine 7 (2020). https://arxiv.org/abs/2009.05383
Afshar, Parnian, et al.: Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. https://arxiv.org/abs/2004.02696
Abbas, Asmaa, Mohammed M. Abdelsamea, and Mohamed Medhat Gaber.: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. (2020). https://arxiv.org/abs/2003.13815
He, Kaiming, et al.: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. https://arxiv.org/abs/1512.03385
Tan, Mingxing, and Quoc V. Le.: Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019). https://arxiv.org/abs/1905.11946
Silva, Pedro, et al.: COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis. Informatics in Medicine Unlocked 20 (2020): 100427. https://www.sciencedirect.com/science/article/pii/S2352914820305773
Touvron, Hugo, et al.: Training data-efficient image transformers & distillation through attention. arXiv preprint arXiv:2012.12877 (2020). https://arxiv.org/abs/2012.12877
Gunraj, Hayden, et al.: COVIDNet-CT: A COVID-19 CT Dataset. https://github.com/haydengunraj/COVIDNet-CT
Hasan, A.M.; AL-Jawad, M.M.; Jalab, H.A.; Shaiba, H.; Ibrahim, R.W.; AL-Shamasneh A.R.: Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features. Entropy 2020, 22, 517. https://doi.org/10.3390/e22050517
Aayush Jaiswal, Neha Gianchandani, Dilbag Singh, Vijay Kumar Manjit Kaur (2020) Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning, Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2020.1788642
Publicado
15/06/2021
Como Citar
COSTA, Gabriel Sousa Silva; PAIVA, Anselmo C.; BRAZ JÚNIOR, Geraldo; FERREIRA, Marco Melo.
COVID-19 automatic diagnosis with CT images using the novel Transformer architecture. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 293-301.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16073.
