Vis enkel innførsel

dc.contributor.advisorEngan, Kjersti
dc.contributor.authorSvendsen, Fredrik
dc.date.accessioned2019-10-04T12:14:13Z
dc.date.available2019-10-04T12:14:13Z
dc.date.issued2019-06
dc.identifier.urihttp://hdl.handle.net/11250/2620358
dc.descriptionMaster's thesis in Automation and signal processingnb_NO
dc.description.abstractBladder cancer is the tenth most common cancer type, where urothelial carcinoma is the most common type of bladder cancer. Bladder cancer has been classified as the most expensive type of cancer per patient, as the need for post-treatment monitoring often lasts the rest of the patient’s life. A pathologist needs to diagnose and evaluate the risk of progression and relapse from analyzing histological images. Recent research shows a correlation between the number of regulatory T-cells and which patients that get progression to a higher cancer grade. Today a computer randomly picks out a sub-set of cells, that is to be manually counted and classified; this will serve as an estimation for regulatory T-cells compared to other cells. This paper proposes a more automated solution to aid in analyzing histological images for the number of regulatory T-cells and other cells present. The two proposed systems are using classical image processing to find and classify the cells based on color and using a convolutional neural network to detect and classify smaller parts of the images. Both systems will attempt to estimate the number of regulatory T-cells compared to other cells. The classical image processing had an underestimation of 4.7% for regulatory T-cells while having a 4.5% overestimation of other cells. The convolutional neural network showed a correlation between the number of classifications and the actual amount of cells but requires further work to be usable.nb_NO
dc.language.isoengnb_NO
dc.publisherUniversity of Stavanger, Norwaynb_NO
dc.relation.ispartofseriesMasteroppgave/UIS-TN-IDE/2019;
dc.subjectinformasjonsteknologinb_NO
dc.subjectsignalbehandlingnb_NO
dc.subjectautomatiseringnb_NO
dc.subjectblærekreftnb_NO
dc.titleImage Processing and Deep Neural Networks for Detection of Immune Cells on Histological Images of Bladder Cancernb_NO
dc.title.alternativeBildebehandling og dype nevrale nett for deteksjon av immunceller på histologiske bilder av blærekreftnb_NO
dc.typeMaster thesisnb_NO
dc.subject.nsiVDP::Technology: 500::Information and communication technology: 550nb_NO


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel