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A dataset of microscopic peripheral blood cell images for development of automatic recognition systems

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dc.creatorAcevedo, Andreaspa
dc.creatorMerino, Annaspa
dc.creatorAlférez, Santiagospa
dc.creatorMolina, Ángelspa
dc.creatorBoldú, Lauraspa
dc.creatorRodellar, Joséspa
dc.date.accessioned2020-05-26T00:05:52Z
dc.date.available2020-05-26T00:05:52Z
dc.date.created2020spa
dc.description.abstractThis article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360 × 363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking. © 2020 The Author(s)eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1016/j.dib.2020.105474
dc.identifier.issn23523409
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23834
dc.language.isoengspa
dc.publisherElsevier Inc.spa
dc.relation.citationTitleData in Brief
dc.relation.citationVolumeVol. 30
dc.relation.ispartofData in Brief, ISSN:23523409, Vol.30,(2020)spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85083451557&doi=10.1016%2fj.dib.2020.105474&partnerID=40&md5=48d8da7e59b8d94c9407b59cd1616f9espa
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.keywordBlood cell automatic recognitionspa
dc.subject.keywordBlood cell imagesspa
dc.subject.keywordBlood cell morphologyspa
dc.subject.keywordDeep learningspa
dc.subject.keywordHematological diagnosisspa
dc.subject.keywordMachine learningspa
dc.titleA dataset of microscopic peripheral blood cell images for development of automatic recognition systemsspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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