Abstract
In medical practice early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important is to differentiate between malignant skin tumours and benign lesions. The aim of this research is classification of skin tumours by analyzing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on hybrid model which combines mathematics and artificial techniques to define strategy to automatic classification for skin tumour images. The proposed hybrid system is tested on well-known HAM10000 data set, and experimental results are compared with similar researches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elgamal, M.: Automatic skin cancer images classification. Int. J. Adv. Comput. Sci. Appl. 4(3), 287–294 (2013)
Swetter, S.M., Tsao, H., Bichakjian, C.K., Curiel-Lewandrowski, C.: Guidelines of care for the management of primary cutaneous melanoma. J. Am. Acad. Dermatol. 80(1), 208–250 (2019)
Messadi, M., Bessaid, A., Taleb-Ahmed, A.: Extraction of specific parameters for skin tumour classification. J. Med. Eng. Technol. 33(4), 288–295 (2009)
Abbes, W., Sellami, D.: High-level features for automatic skin lesions neural network based classification. In: IEEE IPAS 2016: International Image Processing, Application and Systems Conference, Hammamet, Tunisia (2016). https://doi.org/10.1109/ipas.2016.7880148
Krawczyk, B., Simić, D., Simić, S., Woźniak, M.: Automatic diagnosis of primary headaches by machine learning methods. Open Med. 8(2), 157–165 (2013)
Simić, S., Banković, Z., Simić, D., Simić, S.D.: A hybrid clustering approach for diagnosing medical diseases. In: de Cos Juez, F., et al. (eds) Hybrid Artificial Intelligent Systems. HAIS 2018. LNCS, vol. 10870, pp. 741–775. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_62
Simić, S., Banković, Z., Simić, D., Simić, Svetislav D.: Different approaches of data and attribute selection on headache disorder. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, Antonio J. (eds.) IDEAL 2018. LNCS, vol. 11315, pp. 241–249. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03496-2_27
Simić, S., Milutinović, D., Sekulić, S., Simić, D., Simić, S.D., Đorđević, J.: A hybrid case-based reasoning approach to detecting the optimal solution in nurse scheduling problem. Logic J. IGPL (2018). https://doi.org/10.1093/jigpal/jzy047, https://academic.oup.com/jigpal/advance-article/doi/10.1093/jigpal/jzy047/5107037
Aggarwal, C.C.: Data Classification: Algorithms and Applications. Chapman and Hall/CRC, Boca Raton (2014)
Wozniak, M.: Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-642-40997-4
Cruz-Roa, A.A., Arevalo Ovalle, J.E., Madabhushi, A., González Osorio, F.A.: A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 403–410. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_50
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Chuchu, N., et al.: Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma. Cochrane Database Syst. Rev. 12 (2018). https://doi.org/10.1002/14651858.cd013192. Art. No.: CD013192
Lee, T., Gallagher, R., Coldman, A., McLean, D.: Dullrazor®: a software approach to hair removal from images. Comput. Biol. Med. 21(6), 533–543 (1997)
Andreassi, L., et al.: Digital dermoscopy analysis for the differentiation of atypical nevi and early melanoma. Arch. Dermatol. 135, 1459–1465 (1999)
Tran, N.M., Burdejová, P., Osipenko, M., Härdle, W.K.: Principal Component Analysis in an Asymmetric Norm. SFB 649 Discussion Paper 2016–040 (2016). http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2016-040.pdf
Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, New York (2002). https://doi.org/10.1007/b98835
Aussenhofer, M., Dann, S., Langi, Z., Toth, G.: An algorithm to find maximum area polygons circumscribed about a convex polygon. Discrete Appl. Math. 255, 98–108 (2019). https://doi.org/10.1016/j.dam.2018.08.017
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Simić, S., Simić, S.D., Banković, Z., Ivkov-Simić, M., Villar, J.R., Simić, D. (2019). A Hybrid Automatic Classification Model for Skin Tumour Images. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_61
Download citation
DOI: https://doi.org/10.1007/978-3-030-29859-3_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29858-6
Online ISBN: 978-3-030-29859-3
eBook Packages: Computer ScienceComputer Science (R0)