{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T05:06:55Z","timestamp":1735016815747,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"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":[[2024,12,20]]},"abstract":"<jats:p>Image processing has become a central topic in the era of big data, particularly within computer vision, due to the growing volume and diverse resolutions of images. Low-resolution images introduce uncertainty, underscoring the need for high-performance classification methods. Convolutional Neural Networks (CNN), especially the U-Net architecture, are widely applied for pixel-level segmentation due to their encoder-decoder structure. This study applied U-Net on a CT scan image dataset to segment lung images, followed by a CNN classifier to classify lung cancer stages (I, II, IIIa, IIIb). The U-Net model outperformed standard CNNs, achieving 99% in accuracy, precision, sensitivity, and F1 score, compared to the conventional CNN\u2019s 97%, 95%, 97%, and 96%, respectively.<\/jats:p>","DOI":"10.3233\/faia241412","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:19Z","timestamp":1734947299000},"source":"Crossref","is-referenced-by-count":0,"title":["Classification Using U-Net CN on Multi-Resolution CT Scan Image"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6210-7258","authenticated-orcid":false,"given":"Sugiyarto","family":"Surono","sequence":"first","affiliation":[{"name":"Department of Mathematics, FAST UAD Yogyakarta Indonesia"}]},{"given":"Muhammad","family":"Rivaldi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, FAST UAD Yogyakarta Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7622-5060","authenticated-orcid":false,"given":"Nursyiva","family":"Irsalinda","sequence":"additional","affiliation":[{"name":"Department of Mathematics, FAST UAD Yogyakarta Indonesia"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241412","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:20Z","timestamp":1734947300000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241412","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}