Análise Inteligente de Espirais de Arquimedes para Identificação de Indícios de Parkinson
Resumo
A doença de Parkinson é uma condição neurológica que compromete os movimentos, frequentemente associada à redução da dopamina no cérebro. O desenho da espiral de Arquimedes, uma tarefa motora sofisticada que exige coordenação, oferece uma avaliação precisa da função motora. Esta pesquisa apresenta uma metodologia inovadora que utiliza o pré-processamento das imagens, dividindo-as em segmentos, empilhando-os e somando os pixels de cada um para gerar uma imagem final. Com resultados promissores, alcançando 95,4% de acurácia e um F1-Score de 97%, esta abordagem pode ser uma ferramenta valiosa para apoiar especialistas no diagnóstico da doença.Referências
Abdullah, M. I., Hao, S. K., Abdullah, I., and Faizah, S. (2023). Parkinson’s disease symptom detection using hybrid feature extraction and classification model. In 2023 IEEE 14th Control and System Graduate Research Colloquium (ICSGRC), pages 93–98.
Farhah, N. (2024). Utilizing deep learning models in an intelligent spiral drawing classification system for parkinson’s disease classification. Frontiers in Medicine (Lausanne), 11:1453743.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 630–645. Springer.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang, Y., Chaturvedi, K., Nayan, A.-A., Hesamian, M. H., Braytee, A., and Prasad, M. (2024). Early parkinson’s disease diagnosis through hand-drawn spiral and wave analysis using deep learning techniques. Information, 15(4).
Kalia, L. V. and Lang, A. E. (2015). Parkinson’s disease. The Lancet, 386(9996):896–912.
Kamran, I., Naz, S., Razzak, I., and Imran, M. (2021). Handwriting dynamics assessment using deep neural network for early identification of parkinson’s disease. Future Generation Computer Systems, 117:234–244.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI’95, pages 1137–1143, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Mader, K. S. (2019). Parkinson’s Drawings Dataset. Acessado em 11 de janeiro de 2025.
Mitra, S., Pandey, P. M., Pandey, V., and Mayuri, A. (2024). Vision transformer based approach for parkinson’s disease diagnosis. In 2024 IEEE Region 10 Symposium (TENSYMP), pages 1–6.
Pan, S. J. and Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359.
Passo, S. J. G., Oliveira, H. S. d., Pinto, R. A., Quispe, K. G. M., Gusti, R., and Souto, E. J. P. (2021). Classificação de arritmias com paradigma inter e intra paciente utilizando aprendizagem profunda. Journal of Health Informatics, 12.
Pereira, C. R., Pereira, D. R., Silva, F. A., Masieiro, J. P., Weber, S. A. T., Hook, C., and Papa, J. P. (2016a). A new computer vision-based approach to aid the diagnosis of parkinson’s disease. Computer Methods and Programs in Biomedicine, 136:79–88.
Pereira, C. R., Weber, S. A. T., Hook, C., Rosa, G. H., and Papa, J. P. (2016b). Deep learning-aided parkinson’s disease diagnosis from handwritten dynamics. In Proceedings of the SIBGRAPI 2016 - Conference on Graphics, Patterns and Images, pages 340–346.
Poojary, R., Raina, R., and Mondal, A. K. (2021). Effect of data-augmentation on fine-tuned cnn model performance. IAES International Journal of Artificial Intelligence, 10(1):84.
Schlickmann, T. H., Tessari, M. S., Borelli, W. V., Marconi, G. A., Pereira, G. M., Zimmer, E., Noyce, A., Mata, I. F., de Mello Rieder, C. R., dos Santos, D. T., and Schumacher Schuh, A. F. (2025). Prevalence, distribution and future projections of parkinson disease in brazil: insights from the elsi-brazil cohort study. The Lancet Regional Health - Americas, 44:101046.
Soni, R. and Shah, J. (2025). Unveiling the significance of synaptic proteins in parkinson’s pathogenesis: A review. International Journal of Biological Macromolecules, page 140789.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR.
Farhah, N. (2024). Utilizing deep learning models in an intelligent spiral drawing classification system for parkinson’s disease classification. Frontiers in Medicine (Lausanne), 11:1453743.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 630–645. Springer.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang, Y., Chaturvedi, K., Nayan, A.-A., Hesamian, M. H., Braytee, A., and Prasad, M. (2024). Early parkinson’s disease diagnosis through hand-drawn spiral and wave analysis using deep learning techniques. Information, 15(4).
Kalia, L. V. and Lang, A. E. (2015). Parkinson’s disease. The Lancet, 386(9996):896–912.
Kamran, I., Naz, S., Razzak, I., and Imran, M. (2021). Handwriting dynamics assessment using deep neural network for early identification of parkinson’s disease. Future Generation Computer Systems, 117:234–244.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI’95, pages 1137–1143, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Mader, K. S. (2019). Parkinson’s Drawings Dataset. Acessado em 11 de janeiro de 2025.
Mitra, S., Pandey, P. M., Pandey, V., and Mayuri, A. (2024). Vision transformer based approach for parkinson’s disease diagnosis. In 2024 IEEE Region 10 Symposium (TENSYMP), pages 1–6.
Pan, S. J. and Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359.
Passo, S. J. G., Oliveira, H. S. d., Pinto, R. A., Quispe, K. G. M., Gusti, R., and Souto, E. J. P. (2021). Classificação de arritmias com paradigma inter e intra paciente utilizando aprendizagem profunda. Journal of Health Informatics, 12.
Pereira, C. R., Pereira, D. R., Silva, F. A., Masieiro, J. P., Weber, S. A. T., Hook, C., and Papa, J. P. (2016a). A new computer vision-based approach to aid the diagnosis of parkinson’s disease. Computer Methods and Programs in Biomedicine, 136:79–88.
Pereira, C. R., Weber, S. A. T., Hook, C., Rosa, G. H., and Papa, J. P. (2016b). Deep learning-aided parkinson’s disease diagnosis from handwritten dynamics. In Proceedings of the SIBGRAPI 2016 - Conference on Graphics, Patterns and Images, pages 340–346.
Poojary, R., Raina, R., and Mondal, A. K. (2021). Effect of data-augmentation on fine-tuned cnn model performance. IAES International Journal of Artificial Intelligence, 10(1):84.
Schlickmann, T. H., Tessari, M. S., Borelli, W. V., Marconi, G. A., Pereira, G. M., Zimmer, E., Noyce, A., Mata, I. F., de Mello Rieder, C. R., dos Santos, D. T., and Schumacher Schuh, A. F. (2025). Prevalence, distribution and future projections of parkinson disease in brazil: insights from the elsi-brazil cohort study. The Lancet Regional Health - Americas, 44:101046.
Soni, R. and Shah, J. (2025). Unveiling the significance of synaptic proteins in parkinson’s pathogenesis: A review. International Journal of Biological Macromolecules, page 140789.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR.
Publicado
09/06/2025
Como Citar
PEREIRA, Thiago Vítor Gomes; OLIVEIRA, Hygo Sousa de; SANTOS, Eulanda Miranda dos; NÓBREGA, Lígia Reis; ANDRADE, Adriano de Oliveira; GIUSTI, Rafael.
Análise Inteligente de Espirais de Arquimedes para Identificação de Indícios de Parkinson. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 545-556.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7566.
