Edge Detector-Based Hybrid Artificial Neural Network Models for Urinary Bladder Cancer Diagnosis (CROSBI ID 67970)
Prilog u knjizi | izvorni znanstveni rad | međunarodna recenzija
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
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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