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 !

Diagnostic of Bladder Cancer Using Hybrid Neural Networks Based on Edge Detectors (CROSBI ID 694080)

Prilog sa skupa u zborniku | sažetak izlaganja sa skupa

Ivan Lorencin, Josip Španjol, Antun Gršković, Zlatan Car Diagnostic of Bladder Cancer Using Hybrid Neural Networks Based on Edge Detectors // My First Conference Book of Abstracts. Rijeka, 2020. str. 22-23

Podaci o odgovornosti

Ivan Lorencin, Josip Španjol, Antun Gršković, Zlatan Car

hrvatski

Diagnostic of Bladder Cancer Using Hybrid Neural Networks Based on Edge Detectors

Bladder cancer is one of the most common malignant diseases of the urinary tract and is the fourth most common malignant disease in men in Croatia [1]. The diagnostic procedure for bladder cancer usually consists of a biopsy and pathohistological findings. Such an approach can often be invasive and time consuming [2]. For this reason, an endomicroscopic method based on confocal laser endomicroscopy (CLE) supported by artificial intelligence algorithms is being introduced into clinical practice. The application of artificial intelligence in problems of medical image recognition is most often based on the application of artificial neural networks (ANN), most often convolutional neural networks (CNN). The selection of CNN models may require considerable computing resources, which are often unavailable in clinical practice. For this reason, edge detectorsbased neural network hybrid models are being introduced. Such approaches offer a stabile classification performance with much simpler neural network architectures [3]. In this paper, a multi-class classification approach that is based on four classes (high- grade carcinoma, low-grade carcinoma, carcinoma in situ and healthy mucosa) is presented. From obtained results it can be noticed that such an approach offers stabile classification performances for multi-class classification as well, achieving macro AUC of 0.98 and micro AUC of 0.97

Artificial Neural Network, AUC, Bladder Cancer Diagnosis, Edge detector, Hybrid model

nije evidentirano

engleski

Diagnostic of Bladder Cancer Using Hybrid Neural Networks Based on Edge Detectors

nije evidentirano

Artificial Neural Network, AUC, Bladder Cancer Diagnosis, Edge detector, Hybrid model

nije evidentirano

Podaci o prilogu

22-23.

2020.

objavljeno

Podaci o matičnoj publikaciji

My First Conference Book of Abstracts

Rijeka:

Podaci o skupu

4th edition of annual conference for doctoral students of engineering and technology „MY FIRST CONFERENCE“

predavanje

24.09.2020-24.09.2020

Rijeka, Hrvatska

Povezanost rada

Elektrotehnika, Računarstvo, Temeljne medicinske znanosti

Poveznice