A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

Andrés D. Pérez, Oscar Perdomo, Hernán Rios, Francisco Rodríguez, Fabio A. González

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.
Original languageEnglish (US)
Title of host publicationA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
PublisherSpringer VS
ISBN (Electronic)978-3-030-63419-3
ISBN (Print)978-3-030-63418-6
DOIs
StatePublished - Dec 1 2020

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