A lightweight deep learning model for mobile eye fundus image quality assessment

Andrés D. Pérez, Oscar Julian Perdomo Charry, Fabio A. González

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication15th International Symposium on Medical Information Processing and Analysis
EditorsEduardo Romero, Natasha Lepore, Jorge Brieva
PublisherSPIE
ISBN (Electronic)9781510634275
DOIs
StatePublished - Jan 1 2020
Event15th International Symposium on Medical Information Processing and Analysis, SIPAIM 2019 - Medellin, Colombia
Duration: Nov 6 2019Nov 8 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11330
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Symposium on Medical Information Processing and Analysis, SIPAIM 2019
CountryColombia
CityMedellin
Period11/6/1911/8/19

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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