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[{"key": "dc.contributor.advisor", "value": "P\u00f6l\u00f6nen, Ilkka", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Raita-Hakola, Anna-Maria", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Moilanen, Santeri", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2024-01-04T06:45:55Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2024-01-04T06:45:55Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2023", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/92532", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Convolutional neural networks have been successfully used in previous studies to classify medical hyperspectral images. Hyperspectral images are typically classified using semantic segmentation, where each pixel in the image is given a class based on its spectrum. With the help of semantic segmentation, it is possible to see exactly where there is disease or damage in the tissue. Hyperspectral images can also be classified as a whole, in which case a hyperspectral image is given one class based on its spectral properties. However, no previous research has been done on the classification of entire hyperspectral images. The goal of the research was to find out how hyperspectral images can be classified as a whole instead of semantic segmentation. The material of the study was the previously collected lesion material. The work sought and implemented a neural network architecture for the classification of whole hyperspectral images. In addition, the work investigated how the spatial reduction of hyperspectral images affects the classification accuracy. The neural network performed poorly in the classification of hyperspectral images. The classification accuracy improved when the size of the images was reduced spatially. The study gave indications that when classifying whole hyperspectral images, the spatial size should be small in order to maintain good classification accuracy.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Konvoluutioneuroverkkoja on k\u00e4ytetty aiemmissa tutkimuksissa onnistuneesti l\u00e4\u00e4ketieteellisten hyperspektrikuvien luokitteluun. Hyperspektrikuvia luokitellaan tyypillisesti semanttisen segmentoinnin avulla, jossa jokaiselle kuvan pikselille annetaan luokka sen spektrin perusteella. Semanttisen segmentoinnin avulla n\u00e4hd\u00e4\u00e4n tarkasti, miss\u00e4 kohtaa kudosta on sairautta tai vauriota. Hyperspektrikuvia voidaan my\u00f6s luokitella kokonaisina, jolloin hyperspektrikuvalle annetaan yksi luokka sen spektraalisten ominaisuuksien perusteella. Kokonaisten hyperspektrikuvien luokittelemisesta ei kuitenkaan ole tehty aiempaa tutkimusta. Tutkimuksen tavoitteena oli selvitt\u00e4\u00e4, miten hyperspektrikuvia voidaan luokitella semanttisen segmentoinnin sijaan kokonaisina. Tutkimuksen aineistona k\u00e4ytettiin valmiiksi ker\u00e4tty\u00e4 leesioaineistoa. Ty\u00f6ss\u00e4 etsittiin ja toteutettiin neuroverkkoarkkitehtuuri kokonaisten hyperspektrikuvien luokitteluun. Lis\u00e4ksi ty\u00f6ss\u00e4 selvitettiin, miten hyperspektrikuvien pienent\u00e4minen spatiaalisesti vaikuttaa luokittelutarkkuuteen. Neuroverkko suoriutui heikosti hyperspektrikuvien luokittelusta. Luokittelutarkkuus parani kuvien kokoa spatiaalisesti pienennett\u00e4ess\u00e4. Tutkimus antoi viitteit\u00e4 siit\u00e4, ett\u00e4 kokonaisia hyperspektrikuvia luokitellessa spatiaalisen koon tulee olla pieni, jotta luokittelutarkkuus s\u00e4ilyy hyv\u00e4n\u00e4.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2024-01-04T06:45:55Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2024-01-04T06:45:55Z (GMT). No. of bitstreams: 0\n Previous issue date: 2023", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "51", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "konvoluutioneuroverkko", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Kutistettujen hyperspektrikuvien luokittelija", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202401041035", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.copyright", "value": "\u00a9 The Author(s)", "language": null, "element": "rights", "qualifier": "copyright", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "602", "language": null, "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "ihosy\u00f6p\u00e4", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "neuroverkot", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "hyperspektrikuvantaminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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