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Gómez Valverde, Juan José ORCID: https://orcid.org/0000-0002-5073-5908, Antón López, Alfonso, Fatti, Gianluca, Liefers, Bart, Herranz Cabarcos, Alejandra, Santos Lleo, Andres de
ORCID: https://orcid.org/0000-0001-7423-9135, Sánchez, Clara, I and Ledesma Carbayo, María Jesús
ORCID: https://orcid.org/0000-0001-6846-3923
(2019).
Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning.
"Biomedical Optics Express", v. 10
(n. 2);
pp. 892-913.
ISSN 2156-7085.
https://doi.org/10.1364/BOE.10.000892.
Title: | Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning |
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Author/s: |
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Item Type: | Article |
Título de Revista/Publicación: | Biomedical Optics Express |
Date: | 25 January 2019 |
ISSN: | 2156-7085 |
Volume: | 10 |
Subjects: | |
Faculty: | E.T.S.I. Telecomunicación (UPM) |
Department: | Ingeniería Electrónica |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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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.
Item ID: | 64271 |
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DC Identifier: | https://oa.upm.es/64271/ |
OAI Identifier: | oai:oa.upm.es:64271 |
DOI: | 10.1364/BOE.10.000892 |
Official URL: | https://www.osapublishing.org/boe/abstract.cfm?uri... |
Deposited by: | Memoria Investigacion |
Deposited on: | 07 Oct 2020 14:37 |
Last Modified: | 07 Oct 2020 14:37 |