Abstract
The use of machine learning tools for the purpose of medical diagnosis is gradually increasing. This is mainly because the effectiveness of classification has improved a great deal to help medical experts in diagnosing diseases. Such a disease is melanoma malignum, which is a very common type of cancer among humans. In this paper, we use modified version of classical Particle Swarm Optimization (PSO) algorithm, known as the Multi-Elitist PSO (MEPSO) method and support vector machines (SVM) to classify melanoma malignum images previously preprocessed by image segmentation and image feature extraction. The classification accuracy obtained is ca. 96%. The proposed classification method can be developed to an automatic classification process, the performance of which is similar to human perception.
This research has been supported by the Research Grants No. N518 419038 and N518 506439.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Argenziano, G., Fabbrocini, G., Carli, P., DeGiorgi, V., Sammarco, P.E., Delfino, M.: Epiluminescence Microscopy for the Diagnosis of Doubtful Melanocytic Skin Lesions. Archive of Dermatology 134, 1563–1570 (1998)
Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1995)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining Knowledge Discovery 2(2), 121–167 (1998)
Burroni, M., et al.: Melanoma Computer-aided Diagnosis: Reliability and Feasibility Study. Clinical Cancer Research 10, 1881–1886 (2004)
Clerc, M., Kennedy, J.: The Particle Swarm - Explosion, Stability, and Convergence in Multidimensional Complex Space. IEEE Trans. on Evolutionary Computation 6(1), 58–73 (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm, NSGA-II. IEEE Trans. on Evolutionary Computation 6(2) (2002)
Deng, C., Manjunath, B.S.: Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001)
du Vivier, A.W., Williams, H.C., Brett, J.V., Higgins, E.M.: How Do Malignant Melanomas Present and Does This Correlate with the Seven-point Check-list? Clinical Experimental Dermatology 16, 344–347 (1991)
Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support Vector Regression Machines. In: Mozer, M., Jordan, M., Petschke, J. (eds.) Advances in Neural Information Processing System, vol. 9, pp. 155–161. MIT, Cambridge (1997)
Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proc. of IEEE Int. Conference on Evolutionary Computation, vol. 1, pp. 81–86 (2001)
Grigorescu, S., Petkov, N., Kruizinga, P.: Comparison of Texture Features Based on Gabor Filters. IEEE Trans. Image Process 11(10) (2002)
Grzymala-Busse, P., Grzymala-Busse, J.W., Hippe, Z.S.: Melanoma Prediction Using Data Mining System LERS. In: COMPSAC 2001, pp. 615–620 (2001)
Ma, X., Wang, D.: Semantic Modeling Based Image Retrieval System Using Neural Networks. IEEE Int. Conf. on Image Processing, 1165–1168 (2005)
McGovern, T.W., Litaker, M.S.: Clinical Predictors of Malignant Pigmented Lesions: A Comparison of the Glasgow Seven-point Checklist and the American Cancer Society’s ABCDs of Pigmented Lesions. J. Dermatologic Surgey & Oncology 18, 22–26 (1992)
Müller, K.R., Smola, A., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V.: Prediction Time Series with Support Vector Machines. In: Gerstner, W., Germond, A., Hasler, M., Micord, J.D. (eds.) Artificial Neural Networks, Berlin, pp. 999–1004 (1997)
Muezzinoglu, M.K., Żurada, J.M.: RBF-Based Neurodynamic Nearest Neighbor Classification in Real Pattern Space. Pattern Recognition 39(5), 747–760 (2006)
Pehamberger, H., Binder, M., Steiner, A., Wolff, K.: In Vivo Epiluminescence Microscopy: Improvement of Early Diagnosis of Melanoma. Journal of Investigative Dermatology 100, 356S–362S (1993)
Pizzichetta, M.A., Talamini, R., Piccolo, D., et al.: The ABCD Rule of Dermatoscopy Does Not Apply to Small Melanocytic Skin Lesions. Archive of Dermatology 37, 1376–1378 (2001)
Ratnaweera, A., Halgamuge, K.S.: Self Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Trans. on Evolutionary Computation 8(3), 240–254 (2004)
Rubegni, P., Cevenini, G., Burroni, M., et al.: Automated Diagnosis of Pigmented Skin Lesions. Int. J. Cancer 101, 576–580 (2002)
Schindewolf, T., Stolz, W., Albert, R., Abmayr, W., Harms, H.: Classification of Melanocytic Lesions Lesions with Color and Texture Analysis Using Digital Image Processing. American J. of Epidemiology 15, 1–15 (1993)
Stolz, W., Riemann, A., Cognetta, A., et al.: ABCD Rule of Dermatoscopy: A New Practical Method for Early Recognition of Malignant Melanoma. European J. of Dermatology 7, 521–528 (1994)
Tsai, C.F., McGarry, K., Tait, J.: Image Classification Using Hybrid Neural Network. In: Annual ACM Conference on Research and Development in Information Retrieval, pp. 431–432 (2003)
Vapnik, V.: Statistical Learning Theory. John Wiley, New York (1998)
Wang, J.Z., Li, J., Wiederhold, G.: Simplicity Semantic-Sensitive Integrated Matching for Picture Libraries. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 947–963 (2001)
Wang, D., Lim, J.S., Ham, M.M., Lee, B.W.: Learning Similarity for Semantic Images Classification. Neurocomputing 67, 363–368 (2005)
Whited, J.D., Grichnik, J.M.: Does This Patient Have a Mode or a Melanoma. J. of the American Medical Association 279, 696–701 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pia̧tkowska, W., Martyna, J., Nowak, L., Przystalski, K. (2011). A Decision Support System Based on the Semantic Analysis of Melanoma Images Using Multi-elitist PSO and SVM. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-23199-5_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23198-8
Online ISBN: 978-3-642-23199-5
eBook Packages: Computer ScienceComputer Science (R0)