Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Edge Detector-Based Hybrid Artificial Neural Network Models for Urinary Bladder Cancer Diagnosis (CROSBI ID 67970)

Prilog u knjizi | izvorni znanstveni rad | međunarodna recenzija

Lorencin, Ivan ; Anđelić, Nikola ; Baressi Šegota, Sandi ; Musulin, Jelena ; Štifanić, Daniel ; Mrzljak, Vedran ; Španjol, Josip ; Car, Zlatan Edge Detector-Based Hybrid Artificial Neural Network Models for Urinary Bladder Cancer Diagnosis // Studies in Computational Intelligence "Enabling AI Applications in Data Science" / Hassanien, Aboul-Ella ; N. Taha, Mohamed Hamed ; M. Khalifa, Nour Eldeen (ur.). Cham: Springer, 2021. str. 225-245 doi: 10.1007/978-3-030-52067-0_10

Podaci o odgovornosti

Lorencin, Ivan ; Anđelić, Nikola ; Baressi Šegota, Sandi ; Musulin, Jelena ; Štifanić, Daniel ; Mrzljak, Vedran ; Španjol, Josip ; Car, Zlatan

engleski

Edge Detector-Based Hybrid Artificial Neural Network Models for Urinary Bladder Cancer Diagnosis

A standard procedure of ANN utilization in tasks of image recognition and classification includes use of convolutional neural networks (CNN). Such procedure represents a standard approach with applications in various fields of science and technology. Described trend can also be noticed in medical applications. Such approach can be costly from a standpoint of computational resources. For these reasons, the utilization of simpler algorithms can be of significant importance. This feature is particularly emphasized in clinical practice where fewer computing resources are available. Here lies a motive for utilization of hybrid ANN models. In this chapter, an idea of edge detector-based ANN models utilization in diagnosis of urinary bladder cancer is presented.

Edge Detector ; Artificial Neural Network ; Urinary Bladder Cancer

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

225-245.

objavljeno

10.1007/978-3-030-52067-0_10

Podaci o knjizi

Studies in Computational Intelligence "Enabling AI Applications in Data Science"

Hassanien, Aboul-Ella ; N. Taha, Mohamed Hamed ; M. Khalifa, Nour Eldeen

Cham: Springer

2021.

978-3-030-52067-0

1860-949X

1860-9503

Povezanost rada

Elektrotehnika, Kliničke medicinske znanosti, Računarstvo, Temeljne medicinske znanosti

Poveznice
Indeksiranost