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undergraduate thesis
Klasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:545943
Poljak, Dominik
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Poljak, D. (2024). Klasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:545943

Poljak, Dominik. "Klasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:545943

Poljak, Dominik. "Klasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:545943

Poljak, D. (2024). 'Klasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 29 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:545943

Poljak D. Klasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 November 29] Available at: https://urn.nsk.hr/urn:nbn:hr:168:545943

D. Poljak, "Klasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:545943

Metadata
TitleKlasifikacija histoloških snimaka crijeva predtreniranim neuronskim mrežama
Title (english)Classification of histological images of the intestine using pretrained neural network
AuthorDominik Poljak
MentorMarko Subašić (mentor)
Committee memberMarko Subašić (predsjednik povjerenstva)
Committee memberDonik Vršnak (član povjerenstva)
Committee memberIvana Matovinović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu istražujemo primjenu konvolucijskih neuronskih mreža i prijenosnog učenja na zadatku klasifikacije Crohnove bolesti i ulceroznog kolitisa na temelju histoloških snimaka. Fokus je na treniranju predtrenirane mreže EfficientNet, te obradi i prilagodbi ulaznih podataka i njihovoj podjeli na skupove za treniranje, validaciju i testiranje. Rad daje pregled i objašnjenje neuronskih mreža i teorije prijenosnog učenja. Prolazimo kroz postupke stvaranja zakrpa, multi-Otsu automatske segmentacije, otkrivanja bitnih dijelova slike, čišćenja nevažećih podataka i normalizacije. Prikazani su ključni hiperparametri koji utječu na performanse modela te njihov odabir za ovu vrstu problema. Rezultati analize prikazuju učinkovitost različitih modela s različitim konfiguracijama u razlikovanju između dvije navedene bolesti.
Abstract (english)
In this paper, we investigate the application of convolutional neural networks and transfer learning to the task of classifying Crohn's disease and ulcerative colitis based on histological images. The focus is on training a pretrained EfficientNet model, as well as processing and adapting the input data and dividing it into training, validation, and testing sets. The paper provides an overview and explanation of neural networks and the theory of transfer learning. We go through the procedures of patch creation, multi-Otsu automatic segmentation, identification of important image regions, cleaning invalid data, and normalization. Key hyperparameters that affect model performance and their selection for this type of problem are presented. The results of the analysis show the effectiveness of different models with various configurations in distinguishing between the two mentioned diseases.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:545943
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 21:53:44