Image acquisition and automatic quality analysis are fundamental stages and tasks to support anaccurate ocular diagnosis. In particular, when eye fundus image quality is not appropriate, it can hinderthe diagnosis task performed by experts. Portable, smart-phone-based eye fundus image acquisitiondevices have the advantage of their low cost and easy deployment, however, their main disadvantage isthe sacrifice of image quality. This paper presents a deep-learning-based model to assess the eye fundusimage quality which is small enough to be deployed in a smart phone. The model was evaluated in apublic eye fundus dataset with two sets of annotations. The proposed method obtained an accuracy of0.911 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-artmodels, being an alternative for a mobile-based eye fundus quality classification system.
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