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.
