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master's thesis
Klasifikacija patohistoloških slika invazivnog duktalnog karcinoma
2023. urn:nbn:hr:166:148881
Bertović, Dorotea
University of Split
Faculty of Science
Department of Informatics

Cite this document

Bertović, D. (2023). Klasifikacija patohistoloških slika invazivnog duktalnog karcinoma (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:148881

Bertović, Dorotea. "Klasifikacija patohistoloških slika invazivnog duktalnog karcinoma." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:148881

Bertović, Dorotea. "Klasifikacija patohistoloških slika invazivnog duktalnog karcinoma." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:148881

Bertović, D. (2023). 'Klasifikacija patohistoloških slika invazivnog duktalnog karcinoma', Master's thesis, University of Split, Faculty of Science, accessed 15 November 2024, https://urn.nsk.hr/urn:nbn:hr:166:148881

Bertović D. Klasifikacija patohistoloških slika invazivnog duktalnog karcinoma [Master's thesis]. Split: University of Split, Faculty of Science; 2023 [cited 2024 November 15] Available at: https://urn.nsk.hr/urn:nbn:hr:166:148881

D. Bertović, "Klasifikacija patohistoloških slika invazivnog duktalnog karcinoma", Master's thesis, University of Split, Faculty of Science, Split, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:166:148881

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Metadata
TitleKlasifikacija patohistoloških slika invazivnog duktalnog karcinoma
Title (english)Classification of histopathological images of invasive ductal carcinoma
AuthorDorotea Bertović
MentorSaša Mladenović (mentor)
Committee memberSaša Mladenović (predsjednik povjerenstva)
Committee memberDivna Krpan (član povjerenstva)
Committee memberGoran Zaharija (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2023-09-19, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U današnjem svijetu tehnologija raste sve brže, a jedna od njih je umjetna inteligencija koja se može pronaći svuda oko nas te čak i u medicini. Ovaj rad je temeljen na korištenju tehnika strojnog učenja za razvoj modela koji će moći klasificirati invazivni duktalni karcinom na patohistološkim slikama sa naglaskom na metriku odziva. Za izradu modela su korišteni su unaprijed istrenirani modeli VGG16 i ResNet50 uz odmrzavanje nekoliko slojeva te nekoliko dodatnih slojeva na model. Istraženi su i prethodni radovi ovog područja od kojih su neki odabrani za usporedbu sa rezultatima izrađenih modela ovog rada.
Abstract (english)
In today's world, technology is growing faster and faster, and one of them is artificial intelligence, which can be found all around us and even in medicine. This work is based on the use of machine learning techniques to develop a model that will be able to classify invasive ductal carcinoma on histopathological images with an emphasis on response metrics. The pre-trained VGG16 and ResNet50 models were used to create the model, with several layers unfrozen and several additional layers added to the model. Previous works in this area were also investigated, some of which were selected for comparison with the results of the models created in this work.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:148881
Study programmeTitle: Data Science and Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra podatkovne znanosti i inženjerstva (magistar/magistra podatkovne znanosti i inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2023-09-20 07:03:11