Uma Abordagem de Classificação de Imagens Dermatoscópicas Utilizando Aprendizado Profundo com Redes Neurais Convolucionais
Resumo
A identificação automática do melanoma em imagens dermatoscópicas ainda é um problema de difícil solução. Nesse contexto, esse trabalho propõe uma abordagem de classificação dessas imagens, utilizando Aprendizado Profundo com Redes Neurais Convolucionais. As arquiteturas Alexnet e VGG-F foram treinadas com imagens dermatoscópicas, em seguida, utilizadas como modelo para descrição de características, através das últimas camadas totalmente conectadas. A classificação dos dados extraídos foi realizada por alguns algoritmos tradicionais. No melhor resultado da abordagem, o classificados K-Vizinhos mais Próximos atingiu uma acurácia de 91,5%, com índice kappa igual a 0,83, recall e precisão maiores que 0,9.
Referências
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Fleiss, J. L., Levin, B., and Paik, M. C. (2013). Statistical methods for rates and proportions. John Wiley & Sons.
Gutman, D., Codella, N. C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (2016). Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1605.01397.
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Kawahara, J., BenTaieb, A., and Hamarneh, G. (2016). Deep features to classify skin lesions. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, pages 1397–1400. IEEE.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.
Maglogiannis, I. and Doukas, C. N. (2009). Overview of advanced computer vision systems for skin lesions characterization. Information Technology in Biomedicine, IEEE Transactions on, 13(5):721–733.
Majtner, T., Yildirim-Yayilgan, S., and Hardeberg, J. Y. (2016). Combining deep learning and hand-crafted features for skin lesion classification. In Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on, pages 1–6. IEEE.
Mendonça, T., Ferreira, P. M., Marques, J. S., Marcal, A. R., and Rozeira, J. (2013). Ph 2-a dermoscopic image database for research and benchmarking. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 5437–5440. IEEE.
Parkhi, O. M., Vedaldi, A., and Zisserman, A. (2015). Deep face recognition. In BMVC, volume 1, page 6.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252.
Shi, Z. and He, L. (2011). Current status and future potential of neural networks used for medical image processing. Journal of multimedia, 6(3):244–251.
Simard, P. Y., Steinkraus, D., and Platt, J. C. (2003). Best practices for convolutional neural networks applied to visual document analysis. In Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2, ICDAR ’03, pages 958–, Washington, DC, USA. IEEE Computer Society.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Vedaldi, A. and Lenc, K. (2015). Matconvnet: Convolutional neural networks for matlab. In Proceedings of the 23rd ACM international conference on Multimedia, pages 689–692. ACM.
Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., and Smith, J. R. (2015). Deep learning, sparse coding, and svm for melanoma recognition in dermoscopy images. In Machine Learning in Medical Imaging, pages 118–126. Springer.
Fleiss, J. L., Levin, B., and Paik, M. C. (2013). Statistical methods for rates and proportions. John Wiley & Sons.
Gutman, D., Codella, N. C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (2016). Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1605.01397.
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.
INCA (2016). Intituto nacional do câncer - estimativas do câncer para o ano de 2016. [link]. Accessado em 01-05-2016.
Kawahara, J., BenTaieb, A., and Hamarneh, G. (2016). Deep features to classify skin lesions. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, pages 1397–1400. IEEE.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.
Maglogiannis, I. and Doukas, C. N. (2009). Overview of advanced computer vision systems for skin lesions characterization. Information Technology in Biomedicine, IEEE Transactions on, 13(5):721–733.
Majtner, T., Yildirim-Yayilgan, S., and Hardeberg, J. Y. (2016). Combining deep learning and hand-crafted features for skin lesion classification. In Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on, pages 1–6. IEEE.
Mendonça, T., Ferreira, P. M., Marques, J. S., Marcal, A. R., and Rozeira, J. (2013). Ph 2-a dermoscopic image database for research and benchmarking. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 5437–5440. IEEE.
Parkhi, O. M., Vedaldi, A., and Zisserman, A. (2015). Deep face recognition. In BMVC, volume 1, page 6.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252.
Shi, Z. and He, L. (2011). Current status and future potential of neural networks used for medical image processing. Journal of multimedia, 6(3):244–251.
Simard, P. Y., Steinkraus, D., and Platt, J. C. (2003). Best practices for convolutional neural networks applied to visual document analysis. In Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2, ICDAR ’03, pages 958–, Washington, DC, USA. IEEE Computer Society.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Vedaldi, A. and Lenc, K. (2015). Matconvnet: Convolutional neural networks for matlab. In Proceedings of the 23rd ACM international conference on Multimedia, pages 689–692. ACM.
Publicado
02/07/2017
Como Citar
SANTOS, Alan; AIRES, Kelson; VERAS, Rodrigo; UCHÔA, Valeska; SANTOS, Luís.
Uma Abordagem de Classificação de Imagens Dermatoscópicas Utilizando Aprendizado Profundo com Redes Neurais Convolucionais. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 17. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 2010-2019.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2017.3717.
