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undergraduate thesis
Model dubokog učenja za predviđanje Alzheimerove bolesti
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2024. urn:nbn:hr:235:245413
Šegota, Lucian
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

Cite this document

Šegota, L. (2024). Model dubokog učenja za predviđanje Alzheimerove bolesti (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:245413

Šegota, Lucian. "Model dubokog učenja za predviđanje Alzheimerove bolesti." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2024. https://urn.nsk.hr/urn:nbn:hr:235:245413

Šegota, Lucian. "Model dubokog učenja za predviđanje Alzheimerove bolesti." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2024. https://urn.nsk.hr/urn:nbn:hr:235:245413

Šegota, L. (2024). 'Model dubokog učenja za predviđanje Alzheimerove bolesti', Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 11 August 2024, https://urn.nsk.hr/urn:nbn:hr:235:245413

Šegota L. Model dubokog učenja za predviđanje Alzheimerove bolesti [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2024 [cited 2024 August 11] Available at: https://urn.nsk.hr/urn:nbn:hr:235:245413

L. Šegota, "Model dubokog učenja za predviđanje Alzheimerove bolesti", Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:235:245413

Metadata
TitleModel dubokog učenja za predviđanje Alzheimerove bolesti
Title (english)A deep learning model to predict Alzheimer's disease
AuthorLucian Šegota
MentorTomislav Stipančić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2024-02-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract
Alzheimerova bolest (AD) najrašireniji je tip demencije živčanog sustava koji uzrokuje slabljenje mnogih moždanih funkcija (npr. gubitak pamćenja). Budući da je rana dijagnoza najvažniji čimbenik u poboljšanju skrbi za pacijente i ishoda liječenja, neinvazivna rana dijagnoza AD-a privukla je veliku pažnju istraživanja posljednjih godina. Cilj ovog rada je razvoj modela temeljenog na dubokom učenju za preciznu klasifikaciju stadija AD. Koristeći isti prošireni skup podataka, predlažu se dvije arhitekture: arhitektura konvolucijske neuronske mreže (CNN) i arhitektura modela InceptionV3 (transferno učenje). Procjenjuju se te arhitekture dubokog učenja, donose se zaključci iz nalaza i predlažu se poboljšanja za modele. Skup podataka je preuzet s Kagglea koji je javno dostupan i sastoji se od četiri klase koje predstavljaju različite faze Alzheimerove bolesti.
Abstract (english)
Alzheimer’s disease (AD) is the most prevalent type of dementia of the nervous system that causes many brain functions to weaken (eg, memory loss). Since early diagnosis is the most important factor in improving patient care and treatment outcomes, non-invasive early diagnosis of AD has garnered a lot of research attention in recent years. Developing a deep learning-based model for precise AD stage classification is the aim of this thesis. Using the same augmented dataset, two architectures are proposed: a Convolutional Neural Network (CNN) architecture and an InceptionV3 model architecture(transfer learning). This is to evaluate these deep learning architectures, make inferences from the findings, and suggest improvements for the models.. The dataset has been taken from Kaggle which is publicly available and comprises of four classes which represents the various stages of Alzheimer's disease.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:245413
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-02-23 16:17:39