Olga Valenzuela

Prof. Dr. Olga Valenzuela

University of Granada, Granada, Andalusia, Spain
orcid

Research Focus & Metrics

Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2008 in Neurocomputing
Verified
Journal Article
Closed access
Ignacio RojasIgnacio Rojas
Ignacio Rojas
OVOlga Valenzuela
Olga Valenzuela
FRFernando Rojas
Fernando Rojas
Alberto GuillénAlberto Guillén
Alberto Guillén
LHLuis Javier Herrera
Luis Javier Herrera
Miguel PasadasMiguel Pasadas
Miguel Pasadas

1 April 2008 in Fuzzy Sets and Systems
Verified
Journal Article
Closed access
OVOlga Valenzuela
Olga Valenzuela
Ignacio RojasIgnacio Rojas
Ignacio Rojas
FRFernando Rojas
Fernando Rojas
HPHector Pomares
Hector Pomares
LHLuis Javier Herrera
Luis Javier Herrera
Miguel PasadasMiguel Pasadas
Miguel Pasadas

1 October 2007 in Neurocomputing
Verified
Journal Article
Closed access
LHLuis Javier Herrera
Luis Javier Herrera
HPHector Pomares
Hector Pomares
Ignacio RojasIgnacio Rojas
Ignacio Rojas
Alberto GuillénAlberto Guillén
Alberto Guillén
APA. Prieto
A. Prieto
OVOlga Valenzuela
Olga Valenzuela

25 July 2012 in Soft Computing
Verified
Journal Article
Closed access
PDF file
Oresti BanosOresti Banos
Oresti Banos
Miguel Damas HermosoMiguel Damas Hermoso
Miguel Damas Hermoso
HPHector Pomares
Hector Pomares
FRFernando Rojas
Fernando Rojas
BDBlanca Delgado-Marquez
Blanca Delgado-Marquez
OVOlga Valenzuela
Olga Valenzuela

1 August 2005 in Fuzzy Sets and Systems
Verified
Journal Article
Closed access
LHLuis Javier Herrera
Luis Javier Herrera
HPHector Pomares
Hector Pomares
Ignacio RojasIgnacio Rojas
Ignacio Rojas
OVOlga Valenzuela
Olga Valenzuela
APA. Prieto
A. Prieto

9 November 2022 in Applied Sciences
Verified
Journal Article
Open access
PDF file
DMDavid Murcia
David Murcia
IRIgnacio Rojas-Valenzuela
Ignacio Rojas-Valenzuela
OVOlga Valenzuela
Olga Valenzuela
Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system.