Abstract
In this paper, computer-aided diagnosing and classification of melanoid skin lesions is briefly described. The main goal of our research was to elaborate and to present new version of the developed melanoma diagnosis support system, available on the Internet. It is a subsystem of our complementary melanoma diagnosis and classification web center system. Here, we present functionality, structure and operation of this subsystem. In its current version, five learning models are implemented to provide five independent results of diagnosis. Then, a specific voting algorithm is applied to select the correct class (concept) of the diagnosed skin lesion. Developed tool enables users to make early, non-invasive diagnosing of melanocytic lesions. It is possible using built-in set of instructions that animate diagnosis of four basic lesions types: benign nevus, blue nevus, suspicious nevus and melanoma malignant.
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Lucas, R., McMichael, T., Smith, W., Armstrong, B.: Solar ultraviolet radiation. In: Global Burden of Disease from Solar Ultraviolet Radiation. Environmental Burden of Disease Series, vol. 13. World Health Organization, Genewa (2006)
Rigel, D.S., Russak, J., Friedman, R.: The Evolution of Melanoma Diagnosis: 25 Years Beyond the ABCDs. A Cancer Journal for Clinicians 60, 301–316 (2010)
American Cancer Society. Cancer Facts & Figures 2009. American Cancer Society, Atlanta (2009)
Friedman, R.J.: Early detection of malignant melanoma: the role of the physician examination and self examination of the skin. Cancer Journal for Clinicians 35, 130–151 (1985)
Schmid-Saugeon, P., Guillod, J., Thiran, J.P.: Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and Graphics 27, 65–78 (2003)
Blanzieri, E.: Exploiting classifier combination for early melanoma diagnosis support. In: Proc. of the 11th European Conference on Machine Learning, Barcelona, May 31-June 2, pp. 55–62 (2000)
Kirn, T.F.: Reasons Unclear for Worlwide Decline in Melanoma. Skin & Allergy News 31(5), 41–42 (2000)
Taouil, K., Chtourou, Z., Ben Romdhane, N.: A robust system for melanoma diagnosis using heterogeneous image databases. Journal of Biomedical Science and Engineering 3, 576–583 (2010)
Grzymała-Busse, J.W., Hippe, Z.S., Knap, M., Paja, W.: Infoscience Technology: The Impact of Internet Accessible Melanoid Data on Health Issues. In: Smith, F.J. (ed.) Data Science Journal, vol. 4, pp. 77–81 (2005)
Braun-Falco, O., Stolz, W., Bilek, P., Merkle, T., Landthaler, M.: Das Dermatoskop. Eine Vereinfachung der Auflichtmikroskopie von pigmentierten Hautveranderungen. Hautartzt 40, 131–136 (1990)
Stolz, W., Harms, H., Aus, H.M., Abmayr, W., Braun-Falco, O.: Macroscopic diagnosis of melanocytic lesions using color and texture image analysis. J. Invest. Dermatol. 95, 491–497 (1990)
Alvarez, A., Bajcar, S., Brown, F.M., Grzymała-Busse, J.W., Hippe, Z.S.: Optimization of the ABCD Formula Used for Melanoma Diagnosis. In: Kłopotek, M.A., Wierzchoń, S.T. (eds.) Advances In Soft Computing (Intelligent Information Systems and Web Mining), pp. 233–240. Physica-Verlag, Heidelberg (2003)
Hippe, Z.S., Grzymała-Busse, J.W., Błajdo, P., Knap, M., Mroczek, T., Paja, W., Wrzesień, M.: Classification of Medical Images In the Domain of Melanoid Skin Lesions. In: Kurzyński, M., Puchała, E., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems. Advances in Soft Computing, pp. 519–526. Springer, Heidelberg (2005)
Hippe, Z.S., Wrzesień, M.: Some Problems of Uncertainty of Data after the Transfer from Multi-category to Dichotomous Problem Space. In: Burczyski, T., Cholewa, W., Moczulski, W. (eds.) Methods of Artificial Intelligence, pp. 185–189. Silesian University of Technology Edit. Office, Gliwice (2002)
Cudek, P., Grzymała-Busse, J.W., Hippe, Z.S.: Multistrategic Classification System of Melanocytic Skin Lesions: Architecture and First Results. In: Kurzyński, M., Woźniak, M. (eds.) Advances in Intelligent and Soft Computing. Computer Recognition Systems, vol. 3, pp. 381–387. Springer, Heidelberg (2009)
Hippe, Z.S., Grzymała-Busse, J.W., Piatek, Ł.: Synthesis of Medical Images in the Domain of Melanocytic Skin Lesions. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) Advances in Soft Computing (Information Technologies in Biomedicine), pp. 225–231. Springer, Heidelberg (2008)
Paja, W., Wrzesień, M.: Medical datasets analysis: A constructive induction approach. In: Perner, P. (ed.) ICDM 2010. LNCS (LNAI), vol. 6171, pp. 442–449. Springer, Heidelberg (2010)
Hippe, Z.S., Wrzesień, M.: Some problems of uncertainty of data after the tran-sfer from multi-category to dichotomaous problem space. In: Burczyński, T., Cholewa, W., Moczulski, W. (eds.) Methods of Artificial Intelligence, pp. 185–189. Silesian University of Technology Edit. Office, Gliwice (2002)
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Paja, W., Wrzesień, M. (2011). Melanoma Diagnosis and Classification Web Center System: The Non-invasive Diagnosis Support Subsystem. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_8
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DOI: https://doi.org/10.1007/978-3-642-23184-1_8
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