The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning
Open accessAbstract
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.
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Self-citationDermatologist-level classification of skin cancer with deep neural networks
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Cited by
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883
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Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
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Orlando, J. I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 855
A scoping review of transfer learning research on medical image analysis using ImageNet
Morid, M. A., Borjali, A., & Del Fiol, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104115 · Cited by 508
Metadata
- Authors
- Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., Sánchez, C. I., & Ledesma-Carbayo, M. J.
- Venue
- Biomedical Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2019-01-25
- Volume
- 10
- Issue
- 2
- Pages
- 892
- ISSN
- 2156-7085 (ISSN-L)
- OpenAlex
- W2912806832
- Cited by
- 265
- References
- 16
- FWCI
- 22.20
- Type
- journal article
- Language
- en
- Open access
- gold
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- Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., Sánchez, C. I., & Ledesma-Carbayo, M. J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomedical Optics Express. https://doi.org/10.1364/boe.10.000892
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