@inbook{43559336edcc454690b203441e4549c0,
title = "A lightweight deep learning model for mobile eye fundus image quality assessment",
abstract = "Image acquisition and automatic quality analysis are fundamental stages and tasks to support an accurate ocular diagnosis. In particular, when eye fundus image quality is not appropriate, it can hinder the diagnosis task performed by experts. Portable, smart-phone-based eye fundus image acquisition devices have the advantage of their low cost and easy deployment, however, their main disadvantage is the sacrifice of image quality. This paper presents a deep-learning-based model to assess the eye fundus image quality which is small enough to be deployed in a smart phone. The model was evaluated in a public eye fundus dataset with two sets of annotations. The proposed method obtained an accuracy of 0.911 and 0.856, in the binary classification task and the three-classes classification task respectively. Besides, the presented method has a small number of parameters compared to other state-of-the-art models, being an alternative for a mobile-based eye fundus quality classification system.",
author = "P{\'e}rez, {Andr{\'e}s D.} and {Perdomo Charry}, {Oscar Julian} and Gonz{\'a}lez, {Fabio A.}",
year = "2020",
month = jan,
day = "3",
doi = "10.1117/12.2547126",
language = "English (US)",
isbn = "9781510634275",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Eduardo Romero and Natasha Lepore and Jorge Brieva",
booktitle = "15th International Symposium on Medical Information Processing and Analysis",
address = "United States",
note = "15th International Symposium on Medical Information Processing and Analysis, SIPAIM 2019 ; Conference date: 06-11-2019 Through 08-11-2019",
}
