Improving glaucoma diagnosis assembling deep networks and voting schemes
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Sánchez Morales, Adrián; Morales Sánchez, Juan; Kovalyk, Oleksandr; Verdú Monedero, Rafael; Sancho Gómez, José LuisPatrocinadores
This research was funded by Instituto de Salud Carlos III grant number AES2017-PI17/007 and Fundación Séneca grant number 20901/PI/18. The APC was funded by Fundación Séneca grant number 20901/PI/18.Fecha de publicación
2022-06-02Editorial
MDPICita bibliográfica
Sánchez-Morales, A., Morales-Sánchez, J., Kovalyk, O., Verdú-Monedero, R., Sancho-Gómez, J.-L. (2022). Improving Glaucoma Diagnosis Assembling Deep Networks and Voting Schemes. Diagnostics, 12(6), 1382. https://doi.org/10.3390/diagnostics12061382Revisión por pares
siPalabras clave
GlaucomaRetinal images
Deep learning
Ensemble
Soft voting
Resumen
Glaucoma is a group of eye conditions that damage the optic nerve, the health of which is vital for good eyesight. This damage is often caused by higher-than-normal pressure in the eye. In the past few years, the applications of artificial intelligence and data science have increased rapidly in medicine especially in imaging applications. In particular, deep learning tools have been successfully applied obtaining, in some cases, results superior to those obtained by humans. In this article, we present a soft novel ensemble model based on the K-NN algorithm, that combines the probability of class membership obtained by several deep learning models. In this research, three models of different nature (CNN, CapsNets and Convolutional Autoencoders) have been selected searching for diversity. The latent space of these models are combined using the local information provided by the true sample labels and the K-NN algorithm is applied to determine the final decision. The results obtained on two ...
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