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Traditional image processing and deep learning methods are used to automate lesion segmentation. The U-Net is one of the most widely used deep learning architectures for MS lesion segmentation. The images are used in the Fourier domain in the U-Net network, which does not include all its features. Our proposed method combines the HAR wavelet transform and the Dense net-based U-Net. This makes local features and lesions of different sizes more prominent and leads to higher quality segmentation. The proposed method had a better Dice value than the compared methods in the experiments. <\/jats:p>","DOI":"10.1142\/s1469026824500081","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T07:30:25Z","timestamp":1714030225000},"source":"Crossref","is-referenced-by-count":3,"title":["A Hybrid Method for Multiple Sclerosis Lesion Segmentation Using Wavelet and Dense U-Net"],"prefix":"10.1142","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2910-8889","authenticated-orcid":false,"given":"Ali","family":"Alijamaat","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2990-9598","authenticated-orcid":false,"given":"Seyed Mohsen","family":"Mirhosseini","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reyhaneh","family":"Aliakbari","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Sciences, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"S1469026824500081BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2021.102904"},{"key":"S1469026824500081BIB002","doi-asserted-by":"publisher","DOI":"10.1002\/ana.22366"},{"key":"S1469026824500081BIB003","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000410"},{"key":"S1469026824500081BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2017.03.002"},{"key":"S1469026824500081BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.02.035"},{"key":"S1469026824500081BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"S1469026824500081BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-49197-2_7"},{"issue":"1","key":"S1469026824500081BIB008","first-page":"977","volume":"66","author":"Afzal H. 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