Abstract
In this paper is tackled the mammographic image classification problem. From the previous developed CLAP – CLAssification Platform for use with Matlab, several computational paradigms emphasizing neural networks, support vector machines and fuzzy systems, were used to classify mammographic images in two classes, i.e., with or without tumour. To perform the classification task, features must be extracted from the mammographic images. Amongst the methods implemented in CLAP, features obtained from the co-occurrence matrix and wavelets were used, to describe the texture of the region of interest in the image. Results obtained while training and validating the mentioned computational paradigms, show that support vector machines outperform the other two types of classifiers, independently of the features selected.
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Acknowledgments
To the developers of CLAP – CLAssification Platform at the Center of Intelligent Systems of IDMEC/ LAETA. Also a special thanks for the students at Polytechnic Institute of Castelo Branco, Health Equipment Technology degree, that are using the platform for the past 2 years in their classes. This work was partly supported by the Strategic Project, PEst-OE/EME/LA0022/2011, through FCT (under IDMEC-IST, Research Group: IDMEC/LAETA/CSI).
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Gonçalves, P.J.S. (2013). The Classification Platform Applied to Mammographic Images. In: Madureira, A., Reis, C., Marques, V. (eds) Computational Intelligence and Decision Making. Intelligent Systems, Control and Automation: Science and Engineering, vol 61. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4722-7_22
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DOI: https://doi.org/10.1007/978-94-007-4722-7_22
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