Melanoma classification based on ensemble classification of dermoscopy image features

Communications in Computer and Information Science

Volumn 488, Issue , 2014, Pages 291-298

  Schaefer, Gerald ^a   Krawczyk, Bartosz ^b   Emre Celebi, M ^c   Iyatomi, Hitoshi ^d   Hassanien, Aboul Ella ^e  

^a LOUGHBOROUGH UNIVERSITY   (United Kingdom)

^b WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^c LOUISIANA STATE UNIVERSITY   (United States)

^d HOSEI UNIVERSITY   (Japan)

^e CAIRO UNIVERSITY   (Egypt)

Author keywords

Dermoscopy; Ensemble classification; Image features; Melanoma; One class classification; Skin cancer

Indexed keywords

DERMATOLOGY; DIAGNOSIS; DISEASES; IMAGE CLASSIFICATION; LARGE DATASET; LEARNING ALGORITHMS; MACHINE LEARNING; ONCOLOGY; TEXTURES; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

DERMOSCOPY; ENSEMBLE CLASSIFICATION; IMAGE FEATURES; MELANOMA; ONE-CLASS CLASSIFICATION; SKIN CANCERS;

CLASSIFICATION (OF INFORMATION);

EID: 84916208172     PISSN: 18650929     EISSN: 18650937     Source Type: Book Series    
DOI: 10.1007/978-3-319-13461-1_28     Document Type: Conference Paper

References (23)

1. Alpaydin, E.: Combined 5 x 2 CV F test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885–1892 (1999)
2. Argenziano, G., Soyer, H.P., De Giorgi, V.: Dermoscopy: A Tutorial. EDRA Medical Publishing & New Media (2002)
3. Binder, M., Schwarz, M., Winkler, A., Steiner, A., Kaider, A., Wolff, K., Pehamberger, H.: Epiluminescence microscopy. a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Archives of Dermatology 131(3), 286–291 (1995)
4. Błaszczyński, J., Deckert, M., Stefanowski, J., Wilk, S.: Integrating selective preprocessing of imbalanced data with Ivotes ensemble. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 148–157. Springer, Heidelberg (2010)
5. Celebi, M.E., Aslandogan, Y.A., Stoecker, W.V., Iyatomi, H., Oka, H., Chen, X.: Unsupervised border detection in dermoscopy images. Skin Research and Technology 13(4), 454–462 (2007)
6. Celebi, M.E., Kingravi, H., Uddin, B., Iyatomi, H., Aslandogan, A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Computerized Medical Imaging and Graphics 31(6), 362–373 (2007)
7. Celebi, M., Iyatomi, H., Schaefer, G., Stoecker, W.: Lesion border detection in dermoscopy images. Computerized Medical Imaging and Graphics 33(2), 148–153 (2009)
8. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
9. Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107–119. Springer, Heidelberg (2003)
10. Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001)
11. Fleming, M.G., Steger, C., Zhang, J., Gao, J., Cognetta, A.B., Pollak, I., Dyer, C.R.: Techniques for a structural analysis of dermatoscopic imagery. Computerized Medical Imaging and Graphics 22(5), 375–389 (1998)
12. Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)
13. Koch, M.W., Moya, M.M., Hostetler, L.D., Fogler, R.J.: Cueing, feature discovery, and one-class learning for synthetic aperture radar automatic target recognition. Neural Networks 8(7-8), 1081–1102 (1995)
14. Kuncheva, L.I., Whitaker, C.J.: Ten measures of diversity in classifier ensembles: limits for two classifiers. In: IEE Workshop on Intelligent Sensor Processing, pp. 101–106 (2001)
15. Liu, X., Wu, J., Zhou, Z.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Systems, Man and Cybernetics - Part B: Cybernetics 39(2), 539–550 (2009)
16. Menzies, S.W., Crotty, K.A., Ingwar, C., McCarth, W.H.: An atlas of surface microscopy of pigmented skin lesions: dermoscopy, 2nd edn. McGraw-Hill (2003)
17. Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2013. CA: A Cancer Journal for Clinicians 63(1), 11–30 (2013)
18. Steiner, K., Binder, M., Schemper, M., Wolff, K., Pehamberger, H.: Statistical evaluation of epiluminescence dermoscopy criteria for melanocytic pigmented lesions. Journal of the American Academy of Dermatology 29(4), 581–588 (1993)
19. Tax, D.M.J., Duin, R.P.W.: Combining one-class classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 299–308. Springer, Heidelberg (2001)
20. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54(1), 45–66 (2004)
21. Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons (1998)
22. Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 324–331 (2009)
23. Wozniak, M., Zmyslony, M.: Designing combining classifier with trained fuser – analytical and experimental evaluation. Neural Network World 20(7), 925–934 (2010)