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

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dc.creatorPérez A.D.spa
dc.creatorPerdomo Charry, Oscar Juliánspa
dc.creatorGonzález F.A.spa
dc.date.accessioned2020-05-25T23:58:27Z
dc.date.available2020-05-25T23:58:27Z
dc.date.created2020spa
dc.description.abstractImage 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. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1117/12.2547126
dc.identifier.issn1981
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22868
dc.language.isoengspa
dc.publisherSPIEspa
dc.relation.citationTitleProceedings of SPIE - The International Society for Optical Engineering
dc.relation.citationVolumeVol. 11330
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineering, ISSN:1981, Vol.11330,(2020)spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081137400&doi=10.1117%2f12.2547126&partnerID=40&md5=75e39d7e47669c4c2aa8a894bab1b4cdspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordBioinformaticsspa
dc.subject.keywordClassification (of information)spa
dc.subject.keywordDeep learningspa
dc.subject.keywordDiagnosisspa
dc.subject.keywordImage acquisitionspa
dc.subject.keywordLearning systemsspa
dc.subject.keywordMedical imagingspa
dc.subject.keywordMhealthspa
dc.subject.keywordQuality controlspa
dc.subject.keywordSmartphonesspa
dc.subject.keywordBinary classificationspa
dc.subject.keywordClassification tasksspa
dc.subject.keywordEye fundusspa
dc.subject.keywordLearning Based Modelsspa
dc.subject.keywordQuality assessmentspa
dc.subject.keywordQuality classificationspa
dc.subject.keywordReference imagespa
dc.subject.keywordState of the artspa
dc.subject.keywordImage qualityspa
dc.subject.keywordClassificationspa
dc.subject.keywordDeep Learningspa
dc.subject.keywordEye fundusspa
dc.subject.keywordMobile AIspa
dc.subject.keywordNon-reference Image Qualityspa
dc.subject.keywordQuality Assessmentspa
dc.titleA lightweight deep learning model for mobile eye fundus image quality assessmentspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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