Applying FOSS Support Vector Machine and Rough Sets on COVID-19 Cases Triage
Resumo
Free and Open Source Software (FOSS) is attractive for various reasons, calling the attention of developers of systems applied on the field of primary health systems, specially in third world countries. Considering the recent COVID-19 pandemic, FOSS approach has an important role to play on the development of systems on epidemiological surveillance and triage of cases according to the level of priority and taking in account the uncertainty in diagnosis. This paper formulated a model using Support Vector Machine and Rough Sets that demonstrates a proficiency in discerning between COVID, Uncertain, and Normal cases for the triage of cases based on chest X-rays. The results depicts an accuracy of 91.82%.
Palavras-chave:
FOSS, Rough Sets, Support Vector Machine, Machine Learning, COVID-19, Uncertainty
Referências
F. Verbeke, “Free and Open Source Software (FOSS) for Electronic Health Record Management in Developing Countries,” Rwanda Medical Journal, vol. 70, pp. 5–8, Jun. 2013.
“Foss for improving public primary health-care systems in developing countries: A case study of the ehas foundation.” [Online]. Available: [link].
F. Verbeke, “Free and open source software (foss) for electronic health record management in developing countries,” Rwanda Medical Journal, vol. 70, pp. 5–8, 06 2013.
“What is machine learning?” [Online]. Available: [link].
H. R. Bhapkar, P. N. Mahalle, G. R. Shinde, and M. Mahmud, “Rough Sets in COVID-19 to Predict Symptomatic Cases,” in COVID-19: Prediction, Decision-Making, and its Impacts, ser. Lecture Notes on Data Engineering and Communications Technologies, K. Santosh and A. Joshi, Eds. Singapore: Springer, 2021, pp. 57–68. [Online]. Available: https://doi.org/10.1007/978-981-15-9682-7_7
D.-Y. Tsai, Y. Lee, M. Sekiya, and M. Ohkubo, “Medical image classification using genetic-algorithm based fuzzy-logic approach,” Journal of Electronic Imaging, vol. 13, no. 4, pp. 780–788, Oct. 2004, publisher: SPIE.
A. Khan, J.-P. Li, and R. A. Shaikh, “Medical image processing using fuzzy logic,” in 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Dec. 2015, pp. 163–167.
N. Sabri, R. Hamzah, S. Ibrahim, and K. a. F. Abu Samah, “COVID-19 detection for chest X-ray images using local binary pattern,” International Journal of Emerging Trends in Engineering Research, pp. 78–81, 2020. [Online]. Available: https://doi.org/10.30534/ijeter/2020/1281.12020
Z. Camlica, H. R. Tizhoosh, and F. Khalvati, “Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data,” in 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Dec. 2015, pp. 128–132, arXiv:1509.04619 [cs]. [Online]. Available: [link]
W. Scacchi, “Free/open source software development,” in Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, ser. ESEC-FSE ’07. New York, NY, USA: Association for Computing Machinery, Sep. 2007, pp. 459–468. [Online]. Available: https://dl.acm.org/doi/10.1145/1287624.1287689
K. Crowston, K. Wei, J. Howison, and A. Wiggins, “Free/Libre open-source software development: What we know and what we do not know,” ACM Computing Surveys, vol. 44, no. 2, pp. 7:1–7:35, Mar. 2008. [Online]. Available: https://dl.acm.org/doi/10.1145/2089125.2089127
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
S. Gunn, “Support Vector Machines for Classification and Regression,” 1998. [Online]. Available: [link].
S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and t. s.-i. contributors, “scikit-image: image processing in Python,” PeerJ, vol. 2, p. e453, Jun. 2014. [Online]. Available: https://doi.org/10.7717/peerj.453
Z. Pawlak, “Rough set theory and its applications,” Journal of Telecommunications and Information Technology, vol. nr 3, pp. 7–10, 2002. [Online]. Available: [link].
“Rough set,” Feb. 2023, page Version ID: 1138326391. [Online]. Available: [link].
S. S. Ribeiro, “TWD,” Apr. 2023, original-date: 2021-06-23T17:31:12Z. [Online]. Available: [link]
S. S. Ribeiro and J. Yao, “Toward a Three-Way Image Classification Model: A Case Study on Corn Grain Images,” in 2019 IEEE International Symposium on Multimedia (ISM). San Diego, CA, USA: IEEE, Dec. 2019, pp. 177–1776. [Online]. Available: [link]
D. Jankowski, “Roughsets-base: Python library for base Roughsets methods.” Feb. 2022. [Online]. Available: [link]
——, “darekjk/roughsets-base,” Apr. 2023, original-date: 2022-02-03T08:30:14Z. [Online]. Available: [link]
V. Hansen, K. Henings, and G. B. Santos, “Implementation of ”Applying FOSS Support Vector Machine and Rough Sets on COVID-19 Cases Triage”,” 2023. [Online]. Available: [link]
T. Rahman, A. Khandakar, Y. Qiblawey, A. Tahir, S. Kiranyaz, S. B. Abul Kashem, M. T. Islam, S. Al Maadeed, S. M. Zughaier, M. S. Khan, and M. E. H. Chowdhury, “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images,” Computers in Biology and Medicine, vol. 132, p. 104319, May 2021. [Online]. Available: [link].
M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. A. Emadi, M. B. I. Reaz, and M. T. Islam, “Can AI Help in Screening Viral and COVID-19 Pneumonia?” IEEE Access, vol. 8, pp. 132 665–132 676, 2020, conference Name: IEEE Access.
M. d. l. I. Vayá, J. M. Saborit, J. A. Montell, A. Pertusa, A. Bustos, M. Cazorla, J. Galant, X. Barber, D. Orozco-Beltrán, F. García-García, M. Caparrós, G. González, and J. M. Salinas, “BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients,” Jun. 2020, arXiv:2006.01174 [cs, eess]. [Online]. Available: [link]
“Foss for improving public primary health-care systems in developing countries: A case study of the ehas foundation.” [Online]. Available: [link].
F. Verbeke, “Free and open source software (foss) for electronic health record management in developing countries,” Rwanda Medical Journal, vol. 70, pp. 5–8, 06 2013.
“What is machine learning?” [Online]. Available: [link].
H. R. Bhapkar, P. N. Mahalle, G. R. Shinde, and M. Mahmud, “Rough Sets in COVID-19 to Predict Symptomatic Cases,” in COVID-19: Prediction, Decision-Making, and its Impacts, ser. Lecture Notes on Data Engineering and Communications Technologies, K. Santosh and A. Joshi, Eds. Singapore: Springer, 2021, pp. 57–68. [Online]. Available: https://doi.org/10.1007/978-981-15-9682-7_7
D.-Y. Tsai, Y. Lee, M. Sekiya, and M. Ohkubo, “Medical image classification using genetic-algorithm based fuzzy-logic approach,” Journal of Electronic Imaging, vol. 13, no. 4, pp. 780–788, Oct. 2004, publisher: SPIE.
A. Khan, J.-P. Li, and R. A. Shaikh, “Medical image processing using fuzzy logic,” in 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Dec. 2015, pp. 163–167.
N. Sabri, R. Hamzah, S. Ibrahim, and K. a. F. Abu Samah, “COVID-19 detection for chest X-ray images using local binary pattern,” International Journal of Emerging Trends in Engineering Research, pp. 78–81, 2020. [Online]. Available: https://doi.org/10.30534/ijeter/2020/1281.12020
Z. Camlica, H. R. Tizhoosh, and F. Khalvati, “Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data,” in 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Dec. 2015, pp. 128–132, arXiv:1509.04619 [cs]. [Online]. Available: [link]
W. Scacchi, “Free/open source software development,” in Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, ser. ESEC-FSE ’07. New York, NY, USA: Association for Computing Machinery, Sep. 2007, pp. 459–468. [Online]. Available: https://dl.acm.org/doi/10.1145/1287624.1287689
K. Crowston, K. Wei, J. Howison, and A. Wiggins, “Free/Libre open-source software development: What we know and what we do not know,” ACM Computing Surveys, vol. 44, no. 2, pp. 7:1–7:35, Mar. 2008. [Online]. Available: https://dl.acm.org/doi/10.1145/2089125.2089127
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
S. Gunn, “Support Vector Machines for Classification and Regression,” 1998. [Online]. Available: [link].
S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and t. s.-i. contributors, “scikit-image: image processing in Python,” PeerJ, vol. 2, p. e453, Jun. 2014. [Online]. Available: https://doi.org/10.7717/peerj.453
Z. Pawlak, “Rough set theory and its applications,” Journal of Telecommunications and Information Technology, vol. nr 3, pp. 7–10, 2002. [Online]. Available: [link].
“Rough set,” Feb. 2023, page Version ID: 1138326391. [Online]. Available: [link].
S. S. Ribeiro, “TWD,” Apr. 2023, original-date: 2021-06-23T17:31:12Z. [Online]. Available: [link]
S. S. Ribeiro and J. Yao, “Toward a Three-Way Image Classification Model: A Case Study on Corn Grain Images,” in 2019 IEEE International Symposium on Multimedia (ISM). San Diego, CA, USA: IEEE, Dec. 2019, pp. 177–1776. [Online]. Available: [link]
D. Jankowski, “Roughsets-base: Python library for base Roughsets methods.” Feb. 2022. [Online]. Available: [link]
——, “darekjk/roughsets-base,” Apr. 2023, original-date: 2022-02-03T08:30:14Z. [Online]. Available: [link]
V. Hansen, K. Henings, and G. B. Santos, “Implementation of ”Applying FOSS Support Vector Machine and Rough Sets on COVID-19 Cases Triage”,” 2023. [Online]. Available: [link]
T. Rahman, A. Khandakar, Y. Qiblawey, A. Tahir, S. Kiranyaz, S. B. Abul Kashem, M. T. Islam, S. Al Maadeed, S. M. Zughaier, M. S. Khan, and M. E. H. Chowdhury, “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images,” Computers in Biology and Medicine, vol. 132, p. 104319, May 2021. [Online]. Available: [link].
M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. A. Emadi, M. B. I. Reaz, and M. T. Islam, “Can AI Help in Screening Viral and COVID-19 Pneumonia?” IEEE Access, vol. 8, pp. 132 665–132 676, 2020, conference Name: IEEE Access.
M. d. l. I. Vayá, J. M. Saborit, J. A. Montell, A. Pertusa, A. Bustos, M. Cazorla, J. Galant, X. Barber, D. Orozco-Beltrán, F. García-García, M. Caparrós, G. González, and J. M. Salinas, “BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients,” Jun. 2020, arXiv:2006.01174 [cs, eess]. [Online]. Available: [link]
Publicado
18/10/2023
Como Citar
HANSEN, Vinícius; HENINGS, Kalyl; SANTOS, Gilmário Barbosa dos.
Applying FOSS Support Vector Machine and Rough Sets on COVID-19 Cases Triage. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 20. , 2023, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 22-27.
DOI: https://doi.org/10.5753/latinoware.2023.236290.
