prikaz prve stranice dokumenta Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura
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master's thesis
Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura
Rijeka: University of Rijeka, Faculty of Engineering, 2019. urn:nbn:hr:190:782064
Musulin, Jelena
University of Rijeka
Faculty of Engineering
Department of Automation and Electronics
Section of Electronics, Robotics and Automation

Institutional repository: Repository of the University of Rijeka, Faculty of Engineering

Cite this document

Musulin, J. (2019). Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:782064

Musulin, Jelena. "Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura." Master's thesis, University of Rijeka, Faculty of Engineering, 2019. https://urn.nsk.hr/urn:nbn:hr:190:782064

Musulin, Jelena. "Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura." Master's thesis, University of Rijeka, Faculty of Engineering, 2019. https://urn.nsk.hr/urn:nbn:hr:190:782064

Musulin, J. (2019). 'Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 02 January 2025, https://urn.nsk.hr/urn:nbn:hr:190:782064

Musulin J. Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2019 [cited 2025 January 02] Available at: https://urn.nsk.hr/urn:nbn:hr:190:782064

J. Musulin, "Izlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:190:782064

Metadata
TitleIzlučivanje značajki kod algoritama za detekciju karcinoma mokraćnog mjehura
Title (english)Determining features in algorithms for urinary bladder cancer detection
AuthorJelena Musulin
MentorZlatan Car (mentor)
Committee memberNino Stojković (predsjednik povjerenstva)
Committee memberRene Prenc (član povjerenstva)
Committee memberViktor Sučić (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Automation and Electronics)
(Section of Electronics, Robotics and Automation)
Rijeka
Defense date and country2019-09-24, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
U prvom dijelu rada bilo je potrebno izlučiti vektor značajki slika karcinoma mokraćnog mjehura pomoću dva algoritma, SIFT (eng. Scale-Invariant Feature Transform) i SURF (eng. Speeded Up Robust Features) algoritma. Rezultati su pokazali da je pomoću SIFT algoritma izlučeno više ključnih točaka što dovodi do drugog dijela rada gdje je potrebno izlučiti značajke iz originalne slike te dvije augumentirane i pronaći značajke koje se podudaraju. Također pri usporedbi vremena potrebnog da se izluče značajke, SURF algoritam je dvostruko brži od SIFT-a. U zadnjem koraku pomoću izlučenih značajki na setu podataka, trenira se umjetna neuronska mreža. Uz optimalnu arhitekturu mreže, veća točnost i manji gubici postigli su se koristeći SIFT algoritam.
Abstract (english)
In the first part of the paper, a feature vector was extracted from bladder cancer images using two algorithms, the SIFT (Scale-Invariant Feature Transform) algorithm and the SURF (Speeded Up Robust Features) algorithm. More keypoints were extracted using the SIFT algorithm, in the second part of the paper it was necessary to extract the features from the original image and the two augmented ones to find features that matched. Also when comparing the time it takes to extract features, the SURF algorithm is twice as fast as SIFT. In the last step, using the extracted features on the data set, the artificial neural network is trained. With optimal network architecture, higher accuracy and lower losses were achieved using the SIFT algorithm.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:782064
Study programmeTitle: Electrical Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike (magistar/magistra inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2020-07-02 13:03:16