Breast Cancer Classification with Wavelet Neural Network
dc.contributor.author | Ucar, Kursad | |
dc.contributor.author | Kocer, Hasan Erdinc | |
dc.date.accessioned | 2020-03-26T19:34:10Z | |
dc.date.available | 2020-03-26T19:34:10Z | |
dc.date.issued | 2017 | |
dc.department | Selçuk Üniversitesi | en_US |
dc.description | 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY | en_US |
dc.description.abstract | In this paper, we propose a Wavelet Neural Network (WNN) classifier for breast cancer. WNN is a new kind of artificial neural network which is coming more popular these days. This method is based on the Wavelet Transform (WT) and classical neural networks. This paper explains how WNN classifies and uses formulas. The results of the experiments made to obtain the best results and the parameters affecting them are presented. In addition, the classical neural network has been trained and tested. The results of these two learning algorithms are also presented in comparison. | en_US |
dc.description.sponsorship | IEEE Turkey Sect, Anatolian Sci | en_US |
dc.identifier.isbn | 978-1-5386-1880-6 | |
dc.identifier.scopusquality | N/A | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.12395/34851 | |
dc.identifier.wos | WOS:000426868700187 | en_US |
dc.identifier.wosquality | N/A | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2017 INTERNATIONAL ARTIFICIAL INTELLIGENCE AND DATA PROCESSING SYMPOSIUM (IDAP) | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.selcuk | 20240510_oaig | en_US |
dc.subject | Wavelet Neural Network | en_US |
dc.subject | breast cancer | en_US |
dc.subject | classification | en_US |
dc.title | Breast Cancer Classification with Wavelet Neural Network | en_US |
dc.type | Conference Object | en_US |