Prepoznavanje malignih tumora u mikroskopskim slikama tumorskog tkiva

undergraduate thesis

• Title (english): Malignant tumor detection in microscopic images of tumor tissue
• Author: Lucija Aleksić
• Mentor: Mirjana Domazet-Lošo (mentor)
• Committee member: Mirjana Domazet-Lošo (predsjednik povjerenstva)
• Committee member: Ivica Botički (član povjerenstva)
• Committee member: Tomislav Jagušt (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-07-15, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Strojno učenje se može koristiti u svrhu brže opće dijagnostike u medicini. U ovom radu strojno učenje korišteno je u analizi slika tumora dojke. Primjenom odgovarajućih algoritama na skup podataka moguće je stvoriti model koji će moći brže i preciznije dati dijagnozu. Slike tkiva dobivenih pod mikroskopom klasificirane su u odgovarajuće klase malignih ili benignih tumora. Za potrebe rada stvorena su dva modela različite složenosti, kao i hibridni model koji je kombinacija složenijeg s referentnim modelom. Modeli su stvoreni i trenirani uz pomoć Pythonove knjižnice TensorFlow. Dobiveni rezultati ukazuju na potencijal složenijeg modela i odlične rezultate hibridnog modela, iako su svi modeli dobar pokušaj rješenja zadanog problema.

Abstract (english)

Machine learning can be used for the purpose of faster general diagnostics in medicine. In this paper, machine learning was used in the analysis of breast tumor images. By applying appropriate algorithms to a set of data, it is possible to create a model that will be able to make a diagnosis faster and more accurately. Microscopic images of tissues obtained under the microscope are classified into appropriate classes of malignant or benign tumors. For the needs of the work, two models of different complexity were created, as well as a hybrid model that is a combination of the more complex model with the referenced model. Models were created and trained with the help of Python's TensorFlow library. The obtained results indicate the potential of a more complex model and the excellent results of the hybrid model, although all models can be considered as a good attempt to solve a given problem.

• Keywords: strojno učenje; konvolucijske neuronske mreže; umjetne neuronske mreže; Keras; TensorFlow; rak dojke; klasifikacija; analiza slika
• Keywords (english): machine learning; convolutional neural networks; artificial neural networks; Keras; TensorFlow; breast cancer; classification; image analysis
• Language: croatian
• URN:NBN: urn:nbn:hr:168:051996
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-15 09:39:12