Classification of chest X-ray images using Machine Learning and Histogram of Oriented Gradients
Resumo
Este trabalho propõe um modelo de aprendizado de máquina para classificar e detectar a presença de pneumonia a partir de uma coleção de amostras de radiografias do tórax. Ao contrário da maioria dos trabalhos que utilizam abordagens de aprendizado profundo para classificar se a imagem é de um pulmão com pneumonia ou não, ou seja, duas classes para assim alcançar um desempenho de classificação notável, este modelo utiliza Histograma de Gradientes Orientados para extrair características de uma determinada imagem de raio-X de tórax e classificá-la em três classes, determinando se uma pessoa está ou não infectada com pneumonia viral ou bacteriana. Apesar de uma maior complexidade e utilização de modelos tradicionais de aprendizado de máquina, a maior acurácia alcançada foi de 91.32% superior a de trabalhos que utilizam redes profundas e buscam resolver o mesmo grau de complexidade.
Referências
Ali, S. S. M., Alsaeedi, A. H., Al-Shammary, D., Alsaeedi, H. H., and Abid, H. W. (2021). Efficient intelligent system for diagnosis pneumonia (sars-covid19) in x-ray images empowered with initial clustering. Indonesian Journal of Electrical Engineering and Computer Science, 22(1):241–251.
Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), volume 1, pages 886–893. Ieee.
Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., and McDonald, C. J. (2016). Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association, 23(2):304–310.
Faceli, K., Lorena, A., Almeida, T., de Carvalho, A., and Gama, J. (2021). Inteligência Artificial: uma abordagem de Aprendizado de Máquina (2a edição). LTC.
Gu, X., Pan, L., Liang, H., and Yang, R. (2018). Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography. In Proceedings of the 3rd International Conference on Multimedia and Image Processing, ICMIP 2018, page 88–93, New York, NY, USA. Association for Computing Machinery.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
Hall, M. A. (1999). Correlation-based feature selection for machine learning. University of Waikato Hamilton.
Hamadi, A. B. (2021). Interactive automation of covid-19 classification through x-ray images using machine learning. Journal of Independent Studies and Research Computing, 18(2).
Huang, P., Park, S., Yan, R., Lee, J., Chu, L. C., Lin, C. T., Hussien, A., Rathmell, J., Thomas, B., Chen, C., et al. (2018). Added value of computer-aided ct image features for early lung cancer diagnosis with small pulmonary nodules: a matched case-control study. Radiology, 286(1):286–295.
Islam, M. T., Aowal, M. A., Minhaz, A. T., and Ashraf, K. (2017). Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850.
Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Xue, Z., Palaniappan, K., Singh, R. K., Antani, S., et al. (2013). Automatic tuberculosis screening using chest radiographs. IEEE transactions on medical imaging, 33(2):233– 245.
Lakhani, P. and Sundaram, B. (2017). Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2):574–582.
Naicker, S., Plange-Rhule, J., Tutt, R. C., and Eastwood, J. B. (2009). Shortage of healthcare workers in developing countries–africa. Ethnicity & disease, 19(1):60.
Narasimhan, V., Brown, H., Pablos-Mendez, A., Adams, O., Dussault, G., Elzinga, G., Nordstrom, A., Habte, D., Jacobs, M., Solimano, G., et al. (2004). Responding to the global human resources crisis. The Lancet, 363(9419):1469–1472.
Rudan, I., Tomaskovic, L., Boschi-Pinto, C., and Campbell, H. (2004). Global estimate of the incidence of clinical pneumonia among children under five years of age. Bulletin of the World Health Organization, 82:895–903.
Shen, D., Wu, G., and Suk, H.-I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19:221–248.
Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., and Ibragimov, B. (2019). Deep neural network ensemble for pneumonia localization from a largescale chest x-ray database. Computers & electrical engineering, 78:388–399.
Stephen, O., Sain, M., Maduh, U. J., and Jeong, D.-U. (2019). An efficient deep learning approach to pneumonia classification in healthcare. Journal of healthcare engineering, 2019.
Wold, S., Esbensen, K., and Geladi, P. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3):37–52.
World Health Organization (2018). Household air pollution and health. Disponível em: [link].
Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), volume 1, pages 886–893. Ieee.
Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., and McDonald, C. J. (2016). Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association, 23(2):304–310.
Faceli, K., Lorena, A., Almeida, T., de Carvalho, A., and Gama, J. (2021). Inteligência Artificial: uma abordagem de Aprendizado de Máquina (2a edição). LTC.
Gu, X., Pan, L., Liang, H., and Yang, R. (2018). Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography. In Proceedings of the 3rd International Conference on Multimedia and Image Processing, ICMIP 2018, page 88–93, New York, NY, USA. Association for Computing Machinery.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
Hall, M. A. (1999). Correlation-based feature selection for machine learning. University of Waikato Hamilton.
Hamadi, A. B. (2021). Interactive automation of covid-19 classification through x-ray images using machine learning. Journal of Independent Studies and Research Computing, 18(2).
Huang, P., Park, S., Yan, R., Lee, J., Chu, L. C., Lin, C. T., Hussien, A., Rathmell, J., Thomas, B., Chen, C., et al. (2018). Added value of computer-aided ct image features for early lung cancer diagnosis with small pulmonary nodules: a matched case-control study. Radiology, 286(1):286–295.
Islam, M. T., Aowal, M. A., Minhaz, A. T., and Ashraf, K. (2017). Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850.
Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Xue, Z., Palaniappan, K., Singh, R. K., Antani, S., et al. (2013). Automatic tuberculosis screening using chest radiographs. IEEE transactions on medical imaging, 33(2):233– 245.
Lakhani, P. and Sundaram, B. (2017). Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2):574–582.
Naicker, S., Plange-Rhule, J., Tutt, R. C., and Eastwood, J. B. (2009). Shortage of healthcare workers in developing countries–africa. Ethnicity & disease, 19(1):60.
Narasimhan, V., Brown, H., Pablos-Mendez, A., Adams, O., Dussault, G., Elzinga, G., Nordstrom, A., Habte, D., Jacobs, M., Solimano, G., et al. (2004). Responding to the global human resources crisis. The Lancet, 363(9419):1469–1472.
Rudan, I., Tomaskovic, L., Boschi-Pinto, C., and Campbell, H. (2004). Global estimate of the incidence of clinical pneumonia among children under five years of age. Bulletin of the World Health Organization, 82:895–903.
Shen, D., Wu, G., and Suk, H.-I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19:221–248.
Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., and Ibragimov, B. (2019). Deep neural network ensemble for pneumonia localization from a largescale chest x-ray database. Computers & electrical engineering, 78:388–399.
Stephen, O., Sain, M., Maduh, U. J., and Jeong, D.-U. (2019). An efficient deep learning approach to pneumonia classification in healthcare. Journal of healthcare engineering, 2019.
Wold, S., Esbensen, K., and Geladi, P. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3):37–52.
World Health Organization (2018). Household air pollution and health. Disponível em: [link].
Publicado
29/11/2021
Como Citar
BARBOSA, Fellipe M. C.; CANUTO, Anne Magaly de P..
Classification of chest X-ray images using Machine Learning and Histogram of Oriented Gradients. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 49-58.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2021.18240.
