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For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field.<\/jats:p>","DOI":"10.3233\/jifs-211386","type":"journal-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T10:56:47Z","timestamp":1627642607000},"page":"7447-7456","source":"Crossref","is-referenced-by-count":7,"title":["Segmentation of wood cell in cross-section using deep convolutional neural networks"],"prefix":"10.1177","volume":"41","author":[{"given":"Halime","family":"Ergun","sequence":"first","affiliation":[{"name":"Necmettin Erbakan University, Seydi\u015fehir Ahmet Cengiz Faculty of Engineering, Computer Engineering, Konya, Turkey"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211386_ref2","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1163\/22941932-90000054","article-title":"A brief review of machine vision in the context of automated wood identification 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