Abstract
This paper presents a computer aided diagnosis system for skin lesions. Diverse parameters or features extracted from fluorescence images are evaluated for cancer diagnosis. The selection of parameters has a significant effect on the cost and accuracy of an automated classifier. The genetic algorithm (GA) performs parameters selection using the classifier of the K-nearest neighbours (KNN). We evaluate the classification performance of each subset of parameters selected by the genetic algorithm. This classification approach is modular and enables easy inclusion and exclusion of parameters. This facilitates the evaluation of their significance related to the skin cancer diagnosis. We have implemented this parameter evaluation scheme adopting a strategy that automatically optimizes the K-nearest neighbours classifier and indicates which features are more relevant for the diagnosis problem.
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References
Carey, J.D.: MD, Skin cancer, Health Smart Quarterly Health Newsletter. University of New Mexico 12(2), 1–3 (2003)
Toro, F., Ros, E., Mota, S., Ortega, J.: Evolutionary algorithms for multi-objetive and multimodal optimization of diagnostic schemes. IEEE Trans. on Biomedical Engineering 53(2), 178–189 (2006)
Mies, C., Bauer, C., Ackermann, G., Bäumler, W., Abels, C., Puntonet, C.G., Alvarez, M.R., Lang, E.W.: Can ICA help classify skin cancer and benign lesions? In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2085, pp. 328–335. Springer, Heidelberg (2001)
Mehrubeoglu, M., Kehtarnavaz, D.N., Marquez, G., Duvic, M., Lihong, V.W.: Skin lesion classification using oblique-incidence diffuse refiectance spectroscopic imaging Applied optics.  41(1), 182–192 (2002)
Sigurdsson, S., Philipsen, P.A., Hansen, L.K., Larsen, J., Gniadecka, M., Wolf, H.C.: Detection of skin cancer by classification of Raman spectra. IEEE Transactions on Biomedical Engineering 51(10), 1784–1793 (2004)
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)
Ganster, H., Pinz, A., Roehrer, R., Wildling, E., Binder, M., Kittler, H.: Automated Melanoma Recognition. IEEE Transactions on Medical Imaging 20(3), 233–239 (2001)
Ackermann, G.: Photophysikalische Grundlagen zur Fluoreszenzdiagnostik von Tumoren der Haut. PhD. Dissertation, University of Regensburg (2001)
Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8(6), 679–698 (1986)
Parker, J.R.: Algorithms for Image Processing and Computer Vision, pp. 23–29. John Wiley & Sons, Inc., New York (1997)
Lim, J.S.: Two-Dimensional Signal and Image Processing, pp. 478–488. Prentice Hall, Englewood Cliffs (1990)
Fisher, B., Perkins, S., Walker, A., Wolfart, E.: Hypermedia Image Processing Reference, University of Edinburgh, UK (1995)
Gonzalez, R., Woods, R.: Digital Image Processing, pp. 414–428. Addison-Wesley, Reading (1992)
Boyle, R., Thomas, R.: Computer Vision: A First Course, pp. 48–50. Blackwell Scientific Publications, Malden (1988)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)
Serra, J.: Image analysis and Mathematical Morphology. Theoretical Advances, vol. II. Academic Press, London (1988)
Li, L., Weinberg, C.R., Darden, T.A., Pedersen, L.G.: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17, 1131–1142 (2001)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Boston, Massachusetts (1989)
Ros, E., RodrÃguez, M.M., Mota, S., Bernier, J.L., Rojas, I., Puntonet, C.G., Lang, E.W.: Skin Lesions Diagnosis Based on Fluorescence Image Processing: Simple Parameters Scanning. In: Dahl, V., Wadler, P. (eds.) PADL 2003. LNCS, vol. 2562, pp. 858–867. Springer, Heidelberg (2002)
Punch, W.F., Goodman, E.D., Pei, M., Chia-Shun, L., Hovland, P., Enbody, R.: Further Research on Feature Selection and Classification Using Genetic Algorithms. International Conference on Genetic Algorithms 93, 557–564 (1993)
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Odeh, S.M., Ros, E., Rojas, I., Palomares, J.M. (2006). Skin Lesion Diagnosis Using Fluorescence Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_58
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DOI: https://doi.org/10.1007/11867661_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
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