{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:35:54Z","timestamp":1779237354206,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683560","type":"print"},{"value":"9781643683577","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,24]]},"abstract":"<jats:p>In this work, the wavelet transformation (WT) under the context of convolution neural network (CNN) is developed and applied for breast cancer detection. The main objective is to investigate the effectiveness of the WCNN pooling architecture when compared to other two famous pooling strategies; max and average pooling, particularly targeting at the features extraction and classifying the phases of breast cancer by avoiding the under and overfitting problems. It is discovered in this work that the combination of WT and CNN outperforms the traditional and typical CNNs (with 96.49% of accuracy 95.81% of precision, 96.73% of recall and 96.23% of F measure).<\/jats:p>","DOI":"10.3233\/faia220426","type":"book-chapter","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T09:26:24Z","timestamp":1669368384000},"source":"Crossref","is-referenced-by-count":5,"title":["Wavelet Pooling Scheme in the Convolution Neural Network (CNN) for Breast Cancer Detection"],"prefix":"10.3233","author":[{"given":"Ratapong","family":"Onjun","sequence":"first","affiliation":[{"name":"School of Mathematics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kittikorn","family":"Sriwichai","sequence":"additional","affiliation":[{"name":"School of Mathematics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narongdech","family":"Dungkratoke","sequence":"additional","affiliation":[{"name":"School of Mathematics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sayan","family":"Kaennakham","sequence":"additional","affiliation":[{"name":"School of Mathematics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T09:26:24Z","timestamp":1669368384000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,24]]},"ISBN":["9781643683560","9781643683577"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220426","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,24]]}}}