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dc.contributor.advisorPölönen, Ilkka
dc.contributor.advisorÄyrämö, Sami
dc.contributor.authorKarhu, Anette
dc.date.accessioned2020-12-07T13:29:11Z
dc.date.available2020-12-07T13:29:11Z
dc.date.issued2020
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/73001
dc.description.abstractAs skin cancer types are a growing concern worldwide, a new screening tool combined with automation may help the clinicians in clinical examinations of lesions. A novel hyperspectral imager prototype has been noted to be a promising non-invasive tool in screening of lesions. Deep learning, especially semantic segmentation models, have brought successful results in other biomedical imaging tasks. Therefore, semantic segmentation could be used to automate the results from the hyperspectral images of lesions. In this thesis we used a novel hyperspectral image dataset of lesions that contained 61 images. The dataset contained 120 different wavebands from the spectral range of 450 − 850 nm with dimensions of 1920×1200 pixels. We implemented two different semantic segmentation models and compared their performance with the novel hyperspectral image data. The models were compared by their ability to segmentate the images and by their ability to classify lesion types from the images. From the implemented models, the combination of ResNet and Unet model architecture (ResNet-Unet) was able to segmentate the images more accurately with f1-score of 92.38 %, whereas the implemented Unet model gained f1-score of 92.17 %. In addition, the ResNet-Unet model classified the lesion types more accurately, and contained only one false negative result in melanoma classification, when the Unet model contained two false negatives in melanoma classification. This study was able to repeat the results of a previous study, where the segmentation model using hyperspectral image data was able to classify melanoma slightly more accurately than the clinicians in a previous study were.en
dc.format.extent85
dc.language.isoen
dc.subject.otherbiomedical image segmentation
dc.subject.otherdeep learning
dc.subject.otherhyperspectral imaging
dc.titleDeep semantic segmentation for skin cancer detection from hyperspectral images
dc.identifier.urnURN:NBN:fi:jyu-202012076948
dc.type.ontasotMaster’s thesisen
dc.type.ontasotPro gradu -tutkielmafi
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.tiedekuntaFaculty of Information Technologyen
dc.contributor.laitosInformaatioteknologiafi
dc.contributor.laitosInformation Technologyen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.rights.copyrightJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.rights.copyrightThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.contributor.oppiainekoodi602
dc.subject.ysomelanooma
dc.subject.ysokuvantaminen
dc.subject.ysoihosyöpä
dc.subject.ysosegmentointi
dc.subject.ysosyöpätaudit
dc.subject.ysokoneoppiminen
dc.subject.yso3D-mallinnus
dc.subject.ysomelanoma
dc.subject.ysoimaging
dc.subject.ysoskin cancer
dc.subject.ysosegmentation
dc.subject.ysocancerous diseases
dc.subject.ysomachine learning
dc.subject.ysoThree-dimensional imaging


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