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Lesion Classification in Mammograms Using Convolutional Neural Networks and Transfer Learning

dc.contributor.authorPerre, Ana Catarina
dc.contributor.authorAlexandre, Luís
dc.contributor.authorFreire, Luís C.
dc.date.accessioned2020-01-09T11:14:43Z
dc.date.available2020-01-09T11:14:43Z
dc.date.issued2018
dc.description.abstractComputer-Aided Detection/Diagnosis (CAD) tools were created to assist the detection and diagnosis of early stage cancers, decreasing false negative rate and improving radiologists’ efficiency. Convolutional Neural Networks (CNNs) are one example of deep learning algorithms that proved to be successful in image classification. In this paper we aim to study the application of CNNs to the classification of lesions in mammograms. One major problem in the training of CNNs for medical applications is the large dataset of images that is often required but seldom available. To solve this problem, we use a transfer learning approach, wich is based on three different networks that were pre-trained on the Imagenet dataset. We then investigate the performance of these pre-trained CNNs and two types of image normalization to classify lesions in mammograms. The best results were obtained using the Caffe reference model for the CNN with no image normalization.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-319-68195-5_40pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8153
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.titleLesion Classification in Mammograms Using Convolutional Neural Networks and Transfer Learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage368pt_PT
oaire.citation.startPage360pt_PT
oaire.citation.volume27pt_PT
person.familyNamePerre
person.familyNameAlexandre
person.givenNameAna Catarina
person.givenNameLuís
person.identifier.ciencia-id2014-0F06-A3E3
person.identifier.orcid0000-0001-6668-2620
person.identifier.orcid0000-0002-5133-5025
person.identifier.ridE-8770-2013
person.identifier.scopus-author-id8847713100
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication6c1bc4bf-7f4c-439b-8978-7d010ddcf3cc
relation.isAuthorOfPublication131ec6eb-b61a-4f27-953f-12e948a43a96
relation.isAuthorOfPublication.latestForDiscovery131ec6eb-b61a-4f27-953f-12e948a43a96

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