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Pregled bibliografske jedinice broj: 1154365

Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery


Lorencin, Ivan; Smolić, Klara; Baressi Šegota, Sandi; Anđelić, Nikola; Štifanić, Daniel; Musulin, Jelena; Markić, Dean; Španjol, Josip; Car, Zlatan
Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery // 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)
Kragujevac, Srbija: IEEE, 2021. str. 1-6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), ostalo)


CROSBI ID: 1154365 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery

Autori
Lorencin, Ivan ; Smolić, Klara ; Baressi Šegota, Sandi ; Anđelić, Nikola ; Štifanić, Daniel ; Musulin, Jelena ; Markić, Dean ; Španjol, Josip ; Car, Zlatan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo

Izvornik
2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) / - Kragujevac, Srbija : IEEE, 2021, 1-6

Skup
2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)

Mjesto i datum
Kragujevac, Srbija, 25-27.10.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Artificial intelligence, Convolutional neural network, Machine learning, Urinary bladder cancer

Sažetak
In this paper, an approach for urinary bladder cancer diagnosis from computer tomography (CT) images based on the application of convolutional neural networks (CNN) is presented. The image data set that consists of three main parts (frontal, horizontal, and sagittal plane) is used. In order to classify images, pre-defined CNN architectures are used. CNN performances are evaluated by using 5-fold cross-validation procedure that gives information about classification and generalization performances. From the presented results, it can be noticed that higher performances are achieved if more complex CNN architectures are used. Higher performances can be noticed regardless of a plane in which images are captured. An increase in performances can be noticed in both classification and generalization context.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Kliničke medicinske znanosti, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)



POVEZANOST RADA


Projekti:
EK-KF-KK.01.1.1.01.0009-2 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima - IJ za napredne kooperativne sustave (DATACROSS) (Petrović, Ivan; Šmuc, Tomislav, EK - KK.01.1.1.01) ( POIROT)
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( POIROT)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( POIROT)
EK-EFRR-KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša, EK - KK.01.2.2.03) ( POIROT)

Ustanove:
Medicinski fakultet, Rijeka,
Tehnički fakultet, Rijeka,
Klinički bolnički centar Rijeka


Citiraj ovu publikaciju:

Lorencin, Ivan; Smolić, Klara; Baressi Šegota, Sandi; Anđelić, Nikola; Štifanić, Daniel; Musulin, Jelena; Markić, Dean; Španjol, Josip; Car, Zlatan
Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery // 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)
Kragujevac, Srbija: IEEE, 2021. str. 1-6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), ostalo)
Lorencin, I., Smolić, K., Baressi Šegota, S., Anđelić, N., Štifanić, D., Musulin, J., Markić, D., Španjol, J. & Car, Z. (2021) Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery. U: 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE).
@article{article, author = {Lorencin, Ivan and Smoli\'{c}, Klara and Baressi \v{S}egota, Sandi and An\djeli\'{c}, Nikola and \v{S}tifani\'{c}, Daniel and Musulin, Jelena and Marki\'{c}, Dean and \v{S}panjol, Josip and Car, Zlatan}, year = {2021}, pages = {1-6}, keywords = {Artificial intelligence, Convolutional neural network, Machine learning, Urinary bladder cancer}, title = {Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery}, keyword = {Artificial intelligence, Convolutional neural network, Machine learning, Urinary bladder cancer}, publisher = {IEEE}, publisherplace = {Kragujevac, Srbija} }
@article{article, author = {Lorencin, Ivan and Smoli\'{c}, Klara and Baressi \v{S}egota, Sandi and An\djeli\'{c}, Nikola and \v{S}tifani\'{c}, Daniel and Musulin, Jelena and Marki\'{c}, Dean and \v{S}panjol, Josip and Car, Zlatan}, year = {2021}, pages = {1-6}, keywords = {Artificial intelligence, Convolutional neural network, Machine learning, Urinary bladder cancer}, title = {Utilization of Convolutional Neural Networks for Urinary Bladder Cancer Diagnosis Recognition From CT Imagery}, keyword = {Artificial intelligence, Convolutional neural network, Machine learning, Urinary bladder cancer}, publisher = {IEEE}, publisherplace = {Kragujevac, Srbija} }




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