Abstract
The identification of melanomas in dermoscopy images is still an up to date challenge. Several Computer Aided-Diagnosis Systems for the early diagnosis of melanomas have been proposed in the last two decades. This chapter presents an approach to diagnose melanomas using Bag-of-features, a classification method based on a local description of the image in small patches. Moreover, a comparison between color and texture descriptors is performed in order to assess their discriminative power. The presented results show that local descriptors allow an accurate representation of dermoscopy images and achieve good classification scores: Sensitivity \(=\) 93 % and Specificity \(=\) 88 %. Furthermore it shows that color descriptors perform better than texture ones in the detection of melanomas.
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Acknowledgments
The authors thank to Dr. Jorge Rozeira for providing the dermoscopy images. This work was supported by Fundação Ciência e Tecnologia in the scope of the grant SFRH/BD/84658/2012 and projects PTDC/SAU-BEB/103471/2008 and PEst-OE/EEI/LA0009/2011.
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Barata, C., Ruela, M., Mendonça, T., Marques, J.S. (2014). A Bag-of-Features Approach for the Classification of Melanomas in Dermoscopy Images: The Role of Color and Texture Descriptors. In: Scharcanski, J., Celebi, M. (eds) Computer Vision Techniques for the Diagnosis of Skin Cancer. Series in BioEngineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39608-3_3
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