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master's thesis
Predviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:813386
Marković, Dora
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Marković, D. (2019). Predviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:813386

Marković, Dora. "Predviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:813386

Marković, Dora. "Predviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:813386

Marković, D. (2019). 'Predviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:813386

Marković D. Predviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 November 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:813386

D. Marković, "Predviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:813386

Metadata
TitlePredviđanje raka dojke temeljem značajki dobivenih iz probirnih mamograma
Title (english)Prediction of Breast Cancer Using Features Extracted From Screening Mamograms
AuthorDora Marković
MentorTomislav Petković (mentor)
Committee memberTomislav Petković (predsjednik povjerenstva)
Committee memberZvonko Kostanjčar (član povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Mamografija je rendgensko snimanje dojke koje se koristi u probiru i dijagnostici raka dojke. Zbog velikog broja pretraga, interpretacija podataka temeljena na strojnom učenju uvelike olakšava rad i poboljšava rezultate. U ovom radu proučavamo dijagnosticiranje raka dojke uz pomoć dva modela, SVM-a i Bayesovih mreža. Značajke su dobivene iz tri javno dostupne baze, CBIS-DDSM, mini-MIAS te Mammographic Mass Data Set. Značajke su promijenjene iz kategoričkih u numeričke, ako je to bilo potrebno. Za oba model je provedena k-unakrsna provjera te su dobiveni optimalni hiperparametri modela. Uočava se da u većini slučajeva SVM daje malo bolje rezultate od Bayesovih mreža.
Abstract (english)
Mammography is the X-ray of the breast that is used in screening and diagnosis of breast cancer. Due to the large number of searches, interpretation of data based on machine learning greatly facilitates screening and improves the results. In this thesis we investigated breast cancer diagnosis with the help of two models, SVM and the Bayesian network. Features are derived from three publicly accessible databases, CBIS-DDSM, mini-MIAS, and Mammographic Mass Data Set. Features were changed from categorical to numeric, if necessary. For both models, a k-fold cross-validation was performed and the optimal model hyperparameters were obtained. In most cases SVM gives better results than the Bayesian network, but the difference in the obtains scores is small.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:813386
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2020-01-07 20:04:33