Abstract
Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.
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References
Ain, Q.U., Xue, B., Al-Sahaf, H., Zhang, M.: Genetic programming for skin cancer detection in dermoscopic images. In: Proceedings of the 2017 Congress on Evolutionary Computation, pp. 2420–2427. IEEE (2017)
Ain, Q.U., Xue, B., Al-Sahaf, H., Zhang, M.: Genetic programming for feature selection and feature construction in skin cancer image classification. In: Geng, X., Kang, B.-H. (eds.) PRICAI 2018. LNCS (LNAI), vol. 11012, pp. 732–745. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97304-3_56
Al-Sahaf, H., Xue, B., Zhang, M.: A multitree genetic programming representation for automatically evolving texture image descriptors. In: Shi, Y. (ed.) SEAL 2017. LNCS, vol. 10593, pp. 499–511. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68759-9_41
Argenziano, G., Fabbrocini, G., et al.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Archiv. Dermatol. 134(12), 1563–1570 (1998)
Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J.: A color and texture based hierarchical k-NN approach to the classification of non-melanoma skin lesions. In: Celebi, M., Schaefer, G. (eds.) Color Medical Image Analysis. LNCVB, vol. 6, pp. 63–86. Springer, Heidelberg (2013). https://doi.org/10.1007/978-94-007-5389-1_4
Esteva, A., Kuprel, B., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Garnavi, R., Aldeen, M., Bailey, J.: Computer-aided diagnosis of melanoma using border-and wavelet-based texture analysis. IEEE Trans. Inf. Technol. Biomed. 16(6), 1239–1252 (2012)
Hall, M., Frank, E., et al.: The WEKA data mining software: an update. SIGKDD Explor. Newslett. 11(1), 10–18 (2009)
Koza, J.R., Poli, R.: A genetic programming tutorial (2003)
Lee, J.H., Ahn, C.W., An, J.: An approach to self-assembling swarm robots using multitree genetic programming. Sci. World J. 2013, 10 (2013)
Lensen, A., Xue, B., Zhang, M.: Generating redundant features with unsupervised multi-tree genetic programming. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., GarcÃa-Sánchez, P. (eds.) EuroGP 2018. LNCS, vol. 10781, pp. 84–100. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77553-1_6
Luke, S.: Essentials of Metaheuristics, 2nd edn. Lulu, Morrisville (2013). http://cs.gmu.edu/~sean/book/metaheuristics/
Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721–733 (2009)
Mendonça, T., Ferreira, et al.: PH2-a dermoscopic image database for research and benchmarking. In: Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5437–5440 (2013)
Muni, D.P., Pal, N.R., Das, J.: A novel approach to design classifiers using genetic programming. IEEE Trans. Evol. Comput. 8(2), 183–196 (2004)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
Oltean, M., Dumitrescu, D.: Multi expression programming. J. Genetic Program. Evol. Mach. (2002). Kluwer, second tour of review
Satheesha, T., Satyanarayana, D., et al.: Melanoma is skin deep: a 3D reconstruction technique for computerized dermoscopic skin lesion classification. IEEE J. Transl. Eng. Health Med. 5, 1–17 (2017)
Stewart, B.W., Wild, C.P., et al.: World cancer report 2014. Health (2017)
Stolz, W., Riemann, A., et al.: ABCD rule of dermatoscopy: a new practical method for early recognition of malignant-melanoma. Eur. J. Dermatol. 4(7), 521–527 (1994)
Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 994–1004 (2017)
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Ain, Q.U., Al-Sahaf, H., Xue, B., Zhang, M. (2018). A Multi-tree Genetic Programming Representation for Melanoma Detection Using Local and Global Features. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_12
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