Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning
Author
Gómez-Valverde, Juan J.
Antón López, Alfonso
Fatti, Gianluca
Liefers, Bart
Herranz, Alejandra
Santos, Andrés
Sánchez, Clara I.
Ledesma-Carbayo, María J.
Publication date
2019-02-01ISSN
2156-7085
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.
Document Type
Article
Document version
Accepted version
Language
English
Subject (CDU)
61 - Medical sciences
617 - Surgery. Orthopaedics. Ophthalmology
Keywords
Glaucoma
Ulls--Malalties
Ojos--Enfermedades
Eye--Diseases
Oftalmologia
Ophthalmology
Oftalmología
Pages
22
Publisher
Optical Society of America
Collection
10;2
Is part of
Biomedical Optics Express
Citation
Gómez-Valverde, Juan J.; Antón López, Alfonso; Fatti, Gianluca; Liefers, Bart; Herranz, Alejandra; Santos, Andrés; Sánchez, Clara I.; Ledesma-Carbayo, María J. «Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning». Biomedical Optics Express, 2019, vol. 10, núm. 2, p. 892-913. Disponible en: <https://www.osapublishing.org/boe/abstract.cfm?uri=boe-10-2-892>. Fecha de acceso: 26 sept. 2019. DOI: https://doi.org/10.1364/BOE.10.000892
This item appears in the following Collection(s)
- Ciències de la Salut [725]
Rights
http://creativecommons.org/licenses/by-nc-nd/4.0/
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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/