Tool to Assist in the Prognosis of covid-19 and Pneumonia in Robust or Restricted Computational Environments
Abstract
Effective screening of patients infected with COVID-19 plays a crucial role in combating this disease, with chest X-ray examination being one of the main approaches. In this study, we developed four models using Convolutional Neural Network (CNN) trained with 30.000 images, capable of classifying a lung X-ray image as normal, with pneumonia or with COVID-19, with an accuracy close to 90%. In addition, we perform a cost-benefit analysis of the models considering the implementation in more restricted systems, such as embedded systems and systems with greater processing power, such as client-server systems.
Keywords:
Covid-19, Pneumonia, Convolutional neural networks, Radiography, Accuracy
References
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Chen N, Zhou M, Dong X, et al (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507-13. 10.1016/S0140-6736(20)30211-7.
Guan WJ, Ni ZY, Liang WH, et al (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med 2020;382:1708-20. 10.1056/NEJMoa2002032
Hernandez, D., Pereira, R. and Georgevia, P. (2020). COVID-19 detection through X-Ray chest images. International Conference Automatics and Informatics (ICAI).
Hossein, Wong, A., Gunraj, H., et al. (2022). “COVIDx CXR-3”, [link]. Fevereiro.
Kermany, D., Zhang, K. and Goldbaum, M. (2018). Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification. v. 2.
Mohammadi, R., SalehiM., Ghaffari, H., et al. (2020). Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images. Journal of Biomedical Physics and Engineering, p.559-568.
ONEIROS (2015). “Keras API Reference – Applications”. 2015. [link]
Organização Mundial da Saúde (2023) “COVID-19 Weekly Epidemiological Update”, [link], Junho.
Ozturk, T., Talo, M.,Yildirim, E., Baloglu U.,Yildirim. et al. (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, v. 121.
Radiological Society of North America (2018) “RSNA Pneumonia Detection Challenge”, [link]. Agosto.
Tangudu, V., Kakarla, J. and Venkateswarlu, I. (2022) “COVID-19 detection from chest x-ray using MobileNet and residual separable convolution block”, Soft Computer, p. 2197-2208
Wang, L., Lin, Z. and Wong, A., (2020) “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images”, Sci Rep. v. 10.
Yang, D., Martinez, C., Visuña, L. et al. (2021) “Detection and analysis of COVID-19 in medical images using deep learning techniques”, Sci Rep. v. 11.
Published
2023-09-25
How to Cite
SILVA, João Felipe Barros da; FERNANDES, Sílvio Roberto.
Tool to Assist in the Prognosis of covid-19 and Pneumonia in Robust or Restricted Computational Environments. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 854-865.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234495.
