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Classification of Melanoma Images with Fisher Vectors and Deep Learning

  • Gastón Liberman
  • Daniel AcevedoEmail author
  • Marta Mejail
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

The present work corresponds to the application of techniques of data mining and deep training of neural networks (deep learning) with the objective of classifying images of moles in ‘Melanomas’ or ‘No Melanomas’. For this purpose an ensemble of three classifiers will be created. The first corresponds to a convolutional network VGG-16, the other two correspond to two hybrid models. Each hybrid model is composed of a VGG-16 input network and a Support Vector Machine (SVM) as a classifier. These models will be trained with Fisher Vectors (FVs) calculated with the descriptors that are the output of the convolutional network aforementioned. The difference between these two last classifiers lies in the fact that one has segmented images as input of the VGG-16 network, while the other uses non-segmented images. Segmentation is done by means of an U-NET network. Finally, we will analyze the performance of the hybrid models: the VGG-16 network and the ensemble that incorporates the three classifiers.

Keywords

Melanoma classification Deep learning Fisher vectors 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gastón Liberman
    • 1
    • 2
  • Daniel Acevedo
    • 1
    • 2
    Email author
  • Marta Mejail
    • 1
    • 2
  1. 1.Facultad de Ciencias Exactas y Naturales, Departamento de ComputaciónUniversidad de Buenos AiresBuenos AiresArgentina
  2. 2.Instituto de Investigación en Ciencias de la Computación (ICC)CONICET-Universidad de Buenos AiresBuenos AiresArgentina

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