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Automated Detection of Melanoma in Dermoscopic Images

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Computer Vision Techniques for the Diagnosis of Skin Cancer

Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

In this chapter a software system for the automated detection of melanoma over dermoscopic images is presented. The analysis is carried out by supporting in the “ABCD Rule” medical algorithm, undertaking the automated detection and characterization of the corresponding indicators. For this purpose the system uses different image processing techniques and three supervised machine learning tasks. To test the robustness of the system the different indicators of the algorithm are tested, obtaining good results of accuracy in all of them, and there also was determined in a direct way the diagnostic capacity of the system, obtaining results of 81.25 % of sensitivity and 77.14 % of specificity. Moreover, the system is also capable of analyzing macroscopic images, having been designd with multiplatform architecture and being firmly oriented to teledermatology, which is increasingly used.

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Acknowledgments

The authors wish to acknowledge the help of the companies GAIA, IMQ and Maser, as well as the support provided by the Basque Country Government Departments of Education and Industry, Commerce and Tourism.

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Authors and Affiliations

  1. DeustoTech-LIFE Laboratory, Faculty of Engineering, University of Deusto, Bilbao, Spain

    Jose Luis García Arroyo & Begoña García Zapirain

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  1. Jose Luis García Arroyo
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  2. Begoña García Zapirain
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Correspondence to Begoña García Zapirain .

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Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. Computer Science dept., Louisiana State University Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

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Arroyo, J.L.G., Zapirain, B.G. (2014). Automated Detection of Melanoma in Dermoscopic Images. 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_6

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