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Title:Klasifikacija medicinskih slik računalniške tomografije s pomočjo hibridnih pristopov strojnega učenja : magistrsko delo
Authors:ID Habjanič, Matej (Author)
ID Žlahtič, Bojan (Mentor) More about this mentor... New window
Files:.pdf MAG_Habjanic_Matej_2026.pdf (2,28 MB)
MD5: 572B5FFAE221BBC4A9C139F67D7DF498
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo obravnava razvoj hibridnega pristopa strojnega učenja za klasifikacijo slik računalniške tomografije pljuč. Namen je izboljšati natančnost diagnostike z združitvijo različnih arhitektur nevronskih mrež, implementiranih v Pythonu, z uporabo knjižnic Tensorflow in Keras. Rezultati kažejo na višjo uspešnost predlaganega modela v primerjavi s sorodnimi deli, identificirane pa so tudi prednosti, slabosti in možnosti za nadaljnji razvoj tovrstnih pristopov.
Keywords:hibridno strojno učenje, klasifikacija slik, računalniška tomografija pljuč, nevronske mreže
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[M. Habjanič]
Year of publishing:2026
Number of pages:1 spletni vir (1 datoteka PDF (XI, 62 str.))
PID:20.500.12556/DKUM-97552 New window
UDC:004.932:004.85(043.2)
COBISS.SI-ID:278529539 New window
Publication date in DKUM:08.05.2026
Views:236
Downloads:33
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:18.03.2026

Secondary language

Language:English
Title:Classification of medical computer tomography images with hybrid machine learning approaches
Abstract:The master's thesis presents the development of a hybrid machine learning approach for the classification of lung computed tomography images. Its purpose is to improve diagnostic accuracy by combining different neural network architectures, implemented in Python, using the Tensorflow and Keras libraries. The results demonstrate the proposed model's higher performance compared to related works, while also identifying the advantages, disadvantages, and possibilities for the further development of such hybrid approaches.
Keywords:hybrid machine learning, image classification, lung computed tomography, neural networks


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