Artículo

Liberman, G.; Acevedo, D.; Mejail, M.; Vera-Rodriguez R.; Fierrez J.; Morales A."Classification of melanoma images with fisher vectors and deep learning" (2019) 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018. 11401 LNCS:732-739
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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. © Springer Nature Switzerland AG 2019.

Registro:

Documento: Artículo
Título:Classification of melanoma images with fisher vectors and deep learning
Autor:Liberman, G.; Acevedo, D.; Mejail, M.; Vera-Rodriguez R.; Fierrez J.; Morales A.
Filiación:Facultad de Ciencias Exactas y Naturales, Departamento de Computación, Universidad de Buenos Aires, Buenos Aires, Argentina
Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
Palabras clave:Deep learning; Fisher vectors; Melanoma classification; Convolution; Data mining; Dermatology; Image classification; Oncology; Support vector machines; Vectors; Convolutional networks; Descriptors; Fisher vectors; Hybrid model; Net networks; Segmented images; Deep learning
Año:2019
Volumen:11401 LNCS
Página de inicio:732
Página de fin:739
DOI: http://dx.doi.org/10.1007/978-3-030-13469-3_85
Handle:http://hdl.handle.net/20.500.12110/paper_03029743_v11401LNCS_n_p732_Liberman
Título revista:23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
Título revista abreviado:Lect. Notes Comput. Sci.
ISSN:03029743
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v11401LNCS_n_p732_Liberman

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Citas:

---------- APA ----------
Liberman, G., Acevedo, D., Mejail, M., Vera-Rodriguez R., Fierrez J. & Morales A. (2019) . Classification of melanoma images with fisher vectors and deep learning. 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, 11401 LNCS, 732-739.
http://dx.doi.org/10.1007/978-3-030-13469-3_85
---------- CHICAGO ----------
Liberman, G., Acevedo, D., Mejail, M., Vera-Rodriguez R., Fierrez J., Morales A. "Classification of melanoma images with fisher vectors and deep learning" . 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 11401 LNCS (2019) : 732-739.
http://dx.doi.org/10.1007/978-3-030-13469-3_85
---------- MLA ----------
Liberman, G., Acevedo, D., Mejail, M., Vera-Rodriguez R., Fierrez J., Morales A. "Classification of melanoma images with fisher vectors and deep learning" . 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, vol. 11401 LNCS, 2019, pp. 732-739.
http://dx.doi.org/10.1007/978-3-030-13469-3_85
---------- VANCOUVER ----------
Liberman, G., Acevedo, D., Mejail, M., Vera-Rodriguez R., Fierrez J., Morales A. Classification of melanoma images with fisher vectors and deep learning. Lect. Notes Comput. Sci. 2019;11401 LNCS:732-739.
http://dx.doi.org/10.1007/978-3-030-13469-3_85