Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

Lorencin, Ivan; Anđelić, Nikola; Španjol, Josip; Car, Zlatan

doi:10.1016/j.artmed.2019.101746

Pregled bibliografske jedinice broj: 1050220

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

Lorencin, Ivan; Anđelić, Nikola; Španjol, Josip; Car, Zlatan

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis // Artificial Intelligence in Medicine, 102 (2020), 101746, 16 doi:10.1016/j.artmed.2019.101746 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1050220 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

Autori
Lorencin, Ivan ; Anđelić, Nikola ; Španjol, Josip ; Car, Zlatan

Izvornik
Artificial Intelligence in Medicine (1873-2860) 102 (2020); 101746, 16

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Artificial intelligence ; Image pre-processing ; Laplacian edge detector ; Multi-layer perceptron ; Urinary bladder cancer

Sažetak
In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre- processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50*50 and 100*100 images were used.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Kliničke medicinske znanosti

POVEZANOST RADA

Projekti:
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) ( CroRIS)
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( CroRIS)
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)