prikaz prve stranice dokumenta Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja
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master's thesis
Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:992423
Popović, Borna
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Popović, B. (2022). Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:992423

Popović, Borna. "Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:992423

Popović, Borna. "Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:992423

Popović, B. (2022). 'Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:992423

Popović B. Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 August 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:992423

B. Popović, "Detekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:992423

Metadata
TitleDetekcija upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja
Title (english)Pneumonia Detection in Chest X-ray Images Using Deep Learning methods
AuthorBorna Popović
MentorAlan Jović (mentor)
Committee memberAlan Jović (predsjednik povjerenstva)
Committee memberAnte Đerek (član povjerenstva)
Committee memberŽeljka Mihajlović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad obrađuje detekciju upale pluća na rendgenskim snimkama uporabom metoda dubokog učenja. Upala pluća je respiratorna bolest koju uzrokuju bakterije ili virusi. Analiza rendgenskih snimaka prsnog koša najčešći je način dijagnosticiranja upale pluća. Razvijena je konvolucijska neuronska mreža koja omogućuje automatsku detekciju upale pluća na rendgenskim snimkama prsnog koša. Objašnjena je svrha i funkcionalnost pojedinih slojeva konvolucijskih mreža te su dobiveni rezultati uspoređeni s rezultatima drugih nedavnih radova. Dobivena je točnost od 93.3% na skupu za testiranje. Predložena metoda mogla bi se koristiti samo kao pomoćni alat za donošenje kliničkih odluka. Konačna odluka ipak treba biti prepuštena stručnoj osobi zbog nedostatka jasnog i pravilnog objašnjenja rezultata koje je predvidio model.
Abstract (english)
The subject of this paper is the detection of pneumonia on chest X-rays using deep learning methods. Pneumonia is a respiratory disease caused by a virus or bacteria. Chest X-ray analysis is the most common way to diagnose pneumonia. A convolutional neural network with automatic detection of pneumonia on chest X-rays has been developed. The purpose and functionality of individual CNN layers is explained, and the obtained results are compared with other recent studies. An accuracy of 93.3% was obtained on the test set. The proposed method could only be used as an assistance tool for clinical decision making. However, the final decision should be left to the expert due to the lack of a proper and clear explanation of the results predicted by the model.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:992423
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-13 13:32:13