Identificação de Parkinson em Imagens Faciais Usando Modelos de Deep Learning Pré-Treinados
Resumo
Este artigo avaliou o uso de modelos de deep learning pré-treinados na classificação de imagens faciais para diferenciar entre indivíduos saudáveis e pacientes com Doença de Parkinson (DP). Utilizamos um dataset composto por 340 imagens de pacientes com DP e 358 imagens de indivíduos saudáveis, aplicando técnicas como validação cruzada por grupos com 5 folds, aumento de dados e ajuste fino. O melhor desempenho foi obtido com o modelo DenseNet-201, que apresentou uma acurácia média de 92,77%, F1-score de 92,48% e índice Kappa de 85,33%. Esses resultados sugerem que as CNNs pré-treinadas são promissoras para a detecção de Parkinson em imagens faciais.
Referências
Hou, X., Zhang, Y., Wang, Y., Wang, X., Zhao, J., Zhu, X., and Su, J. (2021). A markerless 2d video, facial feature recognition-based, artificial intelligence model to assist with screening for parkinson disease: Development and usability study. Journal of Medical Internet Research, 23(11):e29554.
Huang, W., Zhou, Y., Cheung, Y.-m., Zhang, P., Zha, Y., and Pang, M. (2023). Facial expression guided diagnosis of parkinson’s disease via high-quality data augmentation. IEEE Transactions on Multimedia, 25:7037–7050.
Jiang, R., Chazot, P., Pavese, N., Crookes, D., Bouridane, A., and Celebi, M. (2022). Private facial prediagnosis as an edge service for parkinson’s dbs treatment valuation. IEEE Journal of Biomedical and Health Informatics, 26(6):2703–2713.
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159–174.
Lee, H. M. and Koh, S. B. (2015). Many faces of parkinson’s disease: Non-motor symptoms of parkinson’s disease. Journal of Movement Disorders, 8(2):92–97.
Mumuni, A. and Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches. Array, 16:100258.
Pegolo, E., Volpe, D., Cucca, A., Ricciardi, L., and Sawacha, Z. (2022). Quantitative evaluation of hypomimia in parkinson’s disease: A face tracking approach. Sensors, 22(4).
Powers, D. M. W. (2020). Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation.
Rajnoha, M., Mekyska, J., Burget, R., Eliasova, I., Kostalova, M., and Rektorova, I. (2018). Towards identification of hypomimia in parkinson’s disease based on face recognition methods. In 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pages 1–4.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312.
Huang, W., Zhou, Y., Cheung, Y.-m., Zhang, P., Zha, Y., and Pang, M. (2023). Facial expression guided diagnosis of parkinson’s disease via high-quality data augmentation. IEEE Transactions on Multimedia, 25:7037–7050.
Jiang, R., Chazot, P., Pavese, N., Crookes, D., Bouridane, A., and Celebi, M. (2022). Private facial prediagnosis as an edge service for parkinson’s dbs treatment valuation. IEEE Journal of Biomedical and Health Informatics, 26(6):2703–2713.
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159–174.
Lee, H. M. and Koh, S. B. (2015). Many faces of parkinson’s disease: Non-motor symptoms of parkinson’s disease. Journal of Movement Disorders, 8(2):92–97.
Mumuni, A. and Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches. Array, 16:100258.
Pegolo, E., Volpe, D., Cucca, A., Ricciardi, L., and Sawacha, Z. (2022). Quantitative evaluation of hypomimia in parkinson’s disease: A face tracking approach. Sensors, 22(4).
Powers, D. M. W. (2020). Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation.
Rajnoha, M., Mekyska, J., Burget, R., Eliasova, I., Kostalova, M., and Rektorova, I. (2018). Towards identification of hypomimia in parkinson’s disease based on face recognition methods. In 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pages 1–4.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312.
Publicado
11/09/2024
Como Citar
PEREIRA, Caio B. A. A.; BARROS, Pedro H. X. R.; RODRIGUES, José N. A.; ARAÚJO, Paulo H. R.; BORGES, Rodrigo N.; ALMEIDA, Kelson J.; VERAS, Rodrigo M. S..
Identificação de Parkinson em Imagens Faciais Usando Modelos de Deep Learning Pré-Treinados. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 12. , 2024, Parnaíba/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 169-178.
DOI: https://doi.org/10.5753/ercemapi.2024.243751.
