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dc.contributor.authorPölönen, Ilkka
dc.contributor.authorRahkonen, Samuli
dc.contributor.authorAnnala, Leevi
dc.contributor.authorNeittaanmäki, Noora
dc.contributor.editorChoi, Bernard
dc.contributor.editorZeng, Haishan
dc.date.accessioned2019-05-13T07:22:07Z
dc.date.available2019-05-13T07:22:07Z
dc.date.issued2019
dc.identifier.citationPölönen, I., Rahkonen, S., Annala, L., & Neittaanmäki, N. (2019). Convolutional neural networks in skin cancer detection using spatial and spectral domain. In B. Choi, & H. Zeng (Eds.), <i>Proceedings of SPIE Volume 10851 : Photonics in Dermatology and Plastic Surgery 2019</i> (Article 108510B). SPIE, The International Society for Optical Engineering. SPIE conference proceedings, 10851. <a href="https://doi.org/10.1117/12.2509871" target="_blank">https://doi.org/10.1117/12.2509871</a>
dc.identifier.otherCONVID_28979443
dc.identifier.otherTUTKAID_81008
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/63888
dc.description.abstractSkin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSPIE, The International Society for Optical Engineering
dc.relation.ispartofProceedings of SPIE Volume 10851 : Photonics in Dermatology and Plastic Surgery 2019
dc.relation.ispartofseriesSPIE conference proceedings
dc.rightsIn Copyright
dc.subject.otherneural networks
dc.titleConvolutional neural networks in skin cancer detection using spatial and spectral domain
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201904042072
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-04-04T12:15:09Z
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.relation.issn0277-786X
dc.relation.numberinseries10851
dc.type.versionacceptedVersion
dc.rights.copyright© Society of Photo-Optical Instrumentation Engineers (SPIE), 2019.
dc.rights.accesslevelopenAccessfi
dc.relation.conferencePhotonics in Dermatology and Plastic Surgery
dc.subject.ysospektrikuvaus
dc.subject.ysoihosyöpä
dc.subject.ysoneuroverkot
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
jyx.subject.urihttp://www.yso.fi/onto/yso/p13613
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1117/12.2509871
dc.type.okmA4


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