Evaluation of Melanoma Diagnosis using Imbalanced Learning

• Lucas Bezerra Maia UFMA
• Alan Carlos Lima UFMA
• Pedro Thiago Cutrim Santos UFMA
• Nigel da Silva Lima UFMA
• Humberto Oliveira Serra UFMA
• Geraldo Braz Junior UFMA
• João Dallyson Sousa de Almeida UFMA
• Anselmo Cardoso Paiva UFMA

DOI: https://doi.org/10.5753/sbcas.2018.3680

Resumo

Melanoma is the most lethal type of skin cancer when compared to others, but patients have high recovery rates if the disease is discovered in its early stages. Several approaches to automatic detection and diagnosis have been explored by different authors. Training models with the existing data sets has been a difficult task due to the problem of imbalanced data. This work aims to evaluate the performance of machine learning algorithms combined with imbalanced learning techniques, regarding the task of melanoma diagnosis. Preliminary results have shown that features extracted with ResNet Convolutional Neural Network, along with Random Forest, achieved an improvement of sensibility of approximately 21%, after balancing the training data with Synthetic Minority Oversampling TEchnique (SMOTE) and Edited Nearest Neighbor (ENN) rule.

Referências

Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357.

Chollet, F. et al. (2015). Keras. [link].

Codella, N., Nguyen, Q.-B., Pankanti, S., Gutman, D., Helba, B., Halpern, A., and Smith, J. R. (2016). Deep learning ensembles for melanoma recognition in dermoscopy images. arXiv preprint arXiv:1610.04662.

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.

Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F. (2015). Efficient and robust automated machine learning. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 2962–2970. Curran Associates, Inc.

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.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.

Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., and Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18–28.

Ho, T. K. (1995). Random decision forests. In Document analysis and recognition, 1995., proceedings of the third international conference on, volume 1, pages 278–282. IEEE.

Hubel, D. H. and Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology, 195(1):215–243.

Liu, W. and Chawla, S. (2011). Class confidence weighted knn algorithms for imbalanced data sets. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 345–356. Springer.

Lopez, A. R., Giro-i Nieto, X., Burdick, J., and Marques, O. (2017). Skin lesion classification from dermoscopic images using deep learning techniques. In Biomedical Engineering (BioMed), 2017 13th IASTED International Conference on, pages 49–54. IEEE.

Mendonça, T., Ferreira, P. M., Marques, J. S., Marçal, A. R. S., and Rozeira, J. (2013). Ph2 - a dermoscopic image database for research and benchmarking. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013:5437–40.

Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F. V., Avila, S., and Valle, E. (2017). Knowledge transfer for melanoma screening with deep learning. In Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, pages 297–300. IEEE.

Moura, N., Rodrigo, V., Kelson, A., Machado, V., and Santos, L. (2017). Proposta de um descritor híbrido para aprimoramento da identificação automática de melanoma. XXXVII Congresso da Sociedade Brasileira de Computação – 17o WIM - Workshop de Informatica Médica, pages 2034–2043.

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088):533.

Santos, A., Kelson, A., Rodrigo, V., Uchôa, V., and Santos, L. (2017). Uma abordagem de classificação de imagens dermatoscópicas utilizando aprendizado profundo com redes neurais convolucionais. XXXVII Congresso da Sociedade Brasileira de Computação – 17o WIM - Workshop de Informatica Médica, pages 2010–2019.

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Soares, H. B. (2008). Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte. PhD thesis, Universidade Federal do Rio Grande do Norte.

Wilson, D. L. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, 3:408–421.