Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images

Simple item page
dc.contributor.authorGarcía Chávez, Gabriel Enrique
dc.contributor.authorGallardo, Jhair
dc.contributor.authorMauricio, Antoni
dc.contributor.authorLópez, Jorge
dc.contributor.authorDel Carpio, Christian
dc.date.accessioned2019-01-29T22:19:51Z
dc.date.available2019-01-29T22:19:51Z
dc.date.issued2017
dc.description.abstractDiabetic retinopathy is one of the leading causes of blindness. Its damage is associated with the deterioration of blood vessels in retina. Progression of visual impairment may be cushioned or prevented if detected early, but diabetic retinopathy does not present symptoms prior to progressive loss of vision, and its late detection results in irreversible damages. Manual diagnosis is performed on retinal fundus images and requires experienced clinicians to detect and quantify the importance of several small details which makes this an exhaustive and time-consuming task. In this work, we attempt to develop a computer-assisted tool to classify medical images of the retina in order to diagnose diabetic retinopathy quickly and accurately. A neural network, with CNN architecture, identifies exudates, micro-aneurysms and hemorrhages in the retina image, by training with labeled samples provided by EyePACS, a free platform for retinopathy detection. The database consists of 35126 high-resolution retinal images taken under a variety of conditions. After training, the network shows a specificity of 93.65% and an accuracy of 83.68% on validation process. © Springer International Publishing AG 2017.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.identifier.doihttps://doi.org/10.1007/978-3-319-68612-7_72es_PE
dc.identifier.isbnurn:isbn:9783319686110es_PE
dc.identifier.issn3029743es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15806
dc.language.isoenges_PE
dc.publisherSpringer Verlages_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85034233631&doi=10.1007%2f978-3-319-68612-7_72&partnerID=40&md5=a7c8c9d43ac8c0e0498bbbb9d8608442es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectBlood vesselses_PE
dc.subjectConvolutiones_PE
dc.subjectDamage detectiones_PE
dc.subjectDeep learninges_PE
dc.subjectDiagnosises_PE
dc.subjectImage classificationes_PE
dc.subjectLearning systemses_PE
dc.subjectMedical imaginges_PE
dc.subjectNeural networkses_PE
dc.subjectOphthalmologyes_PE
dc.subjectComputer-assisted tooles_PE
dc.subjectConvolutional neural networkes_PE
dc.subjectDiabetic retinopathyes_PE
dc.subjectIrreversible damagees_PE
dc.subjectRetinal fundus imageses_PE
dc.subjectTime-consuming taskses_PE
dc.subjectValidation processes_PE
dc.subjectVisual impairmentes_PE
dc.subjectEye protectiones_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleDetection of diabetic retinopathy based on a convolutional neural network using retinal fundus imageses_PE
dc.typeinfo:eu-repo/semantics/article
Files
Collections
Artículos - Ciencias de la Computación