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dc.contributor.authorBoiko, Oleksandr
dc.contributor.authorHyttinen, Joni
dc.contributor.authorFält, Pauli
dc.contributor.authorJäsberg, Heli
dc.contributor.authorMirhashemi, Arash
dc.contributor.authorKullaa, Arja
dc.contributor.authorHauta-Kasari, Markku
dc.contributor.editor-
dc.date.accessioned2020-06-16T09:46:41Z
dc.date.available2020-06-16T09:46:41Z
dc.date.issued2019
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/8190
dc.description.abstractThe aim of this work is automatic and efficient detection of medically-relevant features from oral and dental hyperspectral images by applying up-to-date deep learning convolutional neural network techniques. This will help dentists to identify and classify unhealthy areas automatically and to prevent the progression of diseases. Hyperspectral imaging approach allows one to do so without exposing the patient to ionizing X-ray radiation. Spectral imaging provides information in the visible and near-infrared wavelength ranges. The dataset used in this paper contains 116 hyperspectral images from 18 patients taken from different viewing angles. Image annotation (ground truth) includes 38 classes in six different sub-groups assessed by dental experts. Mask region-based convolutional neural network (Mask R-CNN) is used as a deep learning model, for instance segmentation of areas. Preliminary results show high potential and accuracy for classification and segmentation of different classes.
dc.language.isoenglanti
dc.publisherSociety for Imaging Science & Technology
dc.relation.ispartof27th Color and Imaging Conference Final Program and Proceedings
dc.relation.urihttp://dx.doi.org/10.2352/issn.2169-2629.2019.27.53
dc.rightsAll rights reserved
dc.titleDeep Learning for Dental Hyperspectral Image Analysis
dc.description.versionpublished version
dc.contributor.departmentSchool of Computing, activities
dc.contributor.departmentSIB-labs -infrastruktuuriyksikön toiminta,School of Medicine / Dentistry
uef.solecris.id66641749en
dc.type.publicationArtikkelit ja abstraktit tieteellisissä konferenssijulkaisuissa
dc.rights.accessrights© 2019 Society for Imaging Science and Technology. Reprinted with permission of IS&T: The Society for Imaging Science and Technology sole copyright owners of, “CIC27: Twenty-seventh Color and Imaging Conference 2019
dc.relation.doi10.2352/issn.2169-2629.2019.27.53
dc.description.reviewstatuspeerReviewed
dc.format.pagerange295-299
dc.relation.isbn978-0-89208-344-2
dc.relation.issn2166-9635
dc.relation.numberinseries2019
dc.rights.accesslevelopenAccess
dc.type.okmA4
uef.solecris.openaccessEi


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