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Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

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dc.contributor.author Gómez-Valverde, Juan J.
dc.contributor.author Antón López, Alfonso
dc.contributor.author Fatti, Gianluca
dc.contributor.author Liefers, Bart
dc.contributor.author Herranz, Alejandra
dc.contributor.author Santos, Andrés
dc.contributor.author Sánchez, V.
dc.contributor.author Ledesma-Carbayo, María J.
dc.date.accessioned 2019-11-11T08:58:53Z
dc.date.issued 2019
dc.identifier.citation Gómez-Valverde JJ, Antón A, Fatti G, Liefers B, Herranz A, Santos A. et al. Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed Opt Express. 2019 Jan 25;10(2):892-913. DOI 10.1364/BOE.10.000892
dc.identifier.issn 2156-7085
dc.identifier.uri http://hdl.handle.net/10230/42817
dc.description.abstract Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks (CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Optical Society of America (OSA)
dc.rights © Gómez-Valverde, Juan J. 2019. Optical Society of America]. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.
dc.subject.other Glaucoma
dc.title Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1364/BOE.10.000892
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
dc.type.version info:eu-repo/semantics/publishedVersion
dc.embargo.liftdate 2020-01-31
dc.date.embargoEnd info:eu-repo/date/embargoEnd/2020-01-31

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