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dc.creatorPérez A.D. 
dc.creatorPerdomo O. 
dc.creatorGonzález F.A. 
dc.date.accessioned2020-05-25T23:58:27Z
dc.date.available2020-05-25T23:58:27Z
dc.date.created2020
dc.identifier.issn1981
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22868
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.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineering, ISSN:1981, Vol.11330,(2020)
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081137400&doi=10.1117%2f12.2547126&partnerID=40&md5=75e39d7e47669c4c2aa8a894bab1b4cd
dc.titleA lightweight deep learning model for mobile eye fundus image quality assessment
dc.typeconferenceObject
dc.publisherSPIE
dc.subject.keywordBioinformatics
dc.subject.keywordClassification (of information)
dc.subject.keywordDeep learning
dc.subject.keywordDiagnosis
dc.subject.keywordImage acquisition
dc.subject.keywordLearning systems
dc.subject.keywordMedical imaging
dc.subject.keywordMhealth
dc.subject.keywordQuality control
dc.subject.keywordSmartphones
dc.subject.keywordBinary classification
dc.subject.keywordClassification tasks
dc.subject.keywordEye fundus
dc.subject.keywordLearning Based Models
dc.subject.keywordQuality assessment
dc.subject.keywordQuality classification
dc.subject.keywordReference image
dc.subject.keywordState of the art
dc.subject.keywordImage quality
dc.subject.keywordClassification
dc.subject.keywordDeep Learning
dc.subject.keywordEye fundus
dc.subject.keywordMobile AI
dc.subject.keywordNon-reference Image Quality
dc.subject.keywordQuality Assessment
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.type.spaDocumento de conferencia
dc.rights.accesoAbierto (Texto Completo)
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doihttps://doi.org/10.1117/12.2547126
dc.relation.citationTitleProceedings of SPIE - The International Society for Optical Engineering
dc.relation.citationVolumeVol. 11330
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR


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