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master's thesis
Semantička segmentacija slika primjenom dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:765015
Damjanić, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Damjanić, M. (2024). Semantička segmentacija slika primjenom dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:765015

Damjanić, Marko. "Semantička segmentacija slika primjenom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:765015

Damjanić, Marko. "Semantička segmentacija slika primjenom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:765015

Damjanić, M. (2024). 'Semantička segmentacija slika primjenom dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:765015

Damjanić M. Semantička segmentacija slika primjenom dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 November 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:765015

M. Damjanić, "Semantička segmentacija slika primjenom dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:765015

Metadata
TitleSemantička segmentacija slika primjenom dubokog učenja
Title (english)Semantic segmentation of images using deep learning
AuthorMarko Damjanić
MentorTomislav Hrkać (mentor)
Committee memberTomislav Hrkać (predsjednik povjerenstva)
Committee memberSiniša Šegvić (član povjerenstva)
Committee memberAlan Jović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-11, Croatia
Abstract
Ovaj rad evaluira tri modela za semantičku segmentaciju gastrointestinalnih polipa na slikama kolonoskopije: U-Net, PraNet i DUCK-Net. Započinje se s objašnjavanjem os- nova dubokih neuronskih mreža i konvolucijskih neuronskih mreža, nakon čega slijedi detaljna analiza arhitekture i prednosti svakog modela. Javno dostupni skupovi poda- taka standardizirani su i pripremljeni za trening i testiranje, s naglaskom na evaluaciju performansi modela korištenjem uobičajenih metrika segmentacije. U-Net je poslužio kao robusni bazni model, dok je PraNet pokazao izvanredne performanse uz efikasne zahtjeve za treniranje. DUCK-Net, iako kompleksan i resursno zahtjevan, nije posti- gao rezultate kakve se očekivalo. Rezultati sugeriraju da U-Net i PraNet imaju značajan potencijal za poboljšanje s modernim tehnikama, dok kompleksnost DUCK-Net-a pred- stavlja izazove. Dobiveni rezultati ističu potencijal modela za semantičku segmentaciju u pomoći medicinskim dijagnozama, pri čemu se PraNet posebno ističe kao obećavajući za aplikacije u stvarnom vremenu.
Abstract (english)
This thesis evaluates three models for the semantic segmentation of gastrointesti- nal polyps in colonoscopy images: U-Net, PraNet, and DUCK-Net. The study begins by explaining the basics of deep neural networks and convolutional neural networks, fol- lowed by a detailed analysis of the architecture and advantages of each model. Publicly available datasets were standardized and prepared for training and testing, with a focus on evaluating model performance using common segmentation metrics. U-Net served as a robust baseline model, while PraNet demonstrated superior performance with effi- cient training requirements. DUCK-Net, although complex and resource-intensive, did not perform as expected. The findings suggest that U-Net and PraNet have significant potential for improvement with modern techniques, whereas DUCK-Net’s complexity poses challenges. The results underscore the potential of semantic segmentation mod- els in aiding medical diagnoses, with PraNet being particularly promising for real-time applications.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:765015
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 20:24:05