Abstract
Computational intelligence is finding more and more applications in computer aided diagnostics, helping doctors to process large quantities of various medical data [Buronni et al. 2004]. In dermatology it is extremely difficult to perform automatic diagnostic differentiation of malignant melanoma based only on dermatoscopic images. Applying artificial intelligence algorithms to explore and search large database of dermatoscopic images allow doctors to semantically filter out image with specified characteristics. This paper presents an semantic approach for characteristic objects classification found in image database of pigment skin lesions, based on radial basis function kernel for artificial neural networks. Presented approach is divided into few parts: JSEG image segmentation [Deng et al. 2001], feature extraction and classification. Prepared features vector consist of color models parts. For classification Artificial Neural Networks and Support Vector Machines are used and their performance is evaluated and compared. Success rates in both cases are greater than 90%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
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. Arch. Dermatl. 134, 1563–1570 (1998)
Buronni, M., Corona, R., Dell’Eva, G., Sera, F., Bono, R., Puddu, P., Perotti, R., Nobile, F., Andreassi, L., Rubegni, P.: Melanoma computer-aided diagnosis: reliability and feasibility study. Clin. Cancer Res. 1881–1886 (2004)
Yu-Chang, C., Wang, H.J., Li, C.F.: Semantic analysis of real-world images using support vector machine. Experts Systems with Applications, 10560–10569 (2009)
Deng, Y., Manjunat, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)
Liu, Y., Zhang, D., Lu, G., Ma, Y.: Region-based image retrieval with high-level semantic color names. In: IEEE Proc. of the Multimedia Modeling Conf., pp. 180–187 (2005)
Menzies, S.W.: Automated epiluminescence microscopy: human vs machine in the diagnosis of melanoma. Arch. Dermatol. 135, 1538–1540 (1999)
Menzies, S.W., Bischof, L., Talbot, H., et al.: The Performance of SolarScan. An automated dermoscopy image analysis instrument for the diagnosis of primary melanome. Archive of Dermatology, 1388–1396 (2005)
Muezzinoglu, M.K., Zurada, J.M.: RBF-based neurodynamic nearest neighbor classification in real pattern space. Pattern Recognition (2006)
Stolz, W., Riemann, A., Cognetta, A.B., Pillet, L., Abmayr, W., Hölzel, D., Bilek, P., Nachbar, F., Landthaler, M., Braun-Falco, O.: ABCD rule of dermatoscopy: a new practical method for early recognition of malignant melanoma. Eur. J. Dermatol. 7, 521–528 (1994)
Wu, Y. C., Lee, Y.S., Yen, S. J., Yang, Y.C.: Robust and efficient multiclass SVM models for phrase pattern recognition. Pattern Recognition 2874–2889 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Przystalski, K., Nowak, L., Ogorzałek, M., Surówka, G. (2012). Applications of Neural Networks in Semantic Analysis of Skin Cancer Images. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds) Human – Computer Systems Interaction: Backgrounds and Applications 2. Advances in Intelligent and Soft Computing, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23172-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-23172-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23171-1
Online ISBN: 978-3-642-23172-8
eBook Packages: EngineeringEngineering (R0)