{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T20:45:36Z","timestamp":1773434736441,"version":"3.50.1"},"reference-count":34,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Lymphoma appears as swollen lymph nodes and weakened immune-protective tissues, frequently resulting in tiredness and loss of weight. Improving the outlook of this malignancy includes using computer-assisted analysis of Positron Emission Tomography (PET) pictures, which identify changes in metabolism. This article presents an Automatic Pre-Segmentation Model (APSM) that uses the Swin Transformer (ST). The APSM accurately separates inputs by recognizing pixel differences caused by changes in metabolism in various tissues and lymph nodes. Training the Swin Transformer system for classification and identification happens simultaneously, focusing mainly on the lymph node area. The model effectively divides the Lymphoma area by examining differences in patterns between regional features and changes in pixels. This segmentation model combines transformer network training to simultaneously learn fractal variations and feature changes, helping to adjust the relationships between training and testing inputs. The segmentation model\u2019s effectiveness comes from its capability to stop training the matching transformer network when it identifies new deviations, alterations, or both. The proposed model achieved 12.68% higher segmentation accuracy, 13.38% improved precision, and reduced overhead, error, and segmentation time by 12.73%, 9.27%, and 10.23%, respectively, outperforming existing methods.<\/jats:p>","DOI":"10.1371\/journal.pone.0329261","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T18:20:51Z","timestamp":1755109251000},"page":"e0329261","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing precision in lymphoma detection with novel deep transformer-based neural networks"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1314-7146","authenticated-orcid":true,"given":"Turki M.","family":"Alanazi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"pone.0329261.ref001","unstructured":"Frood R, Willaime JMY, Miles B, Chambers G, Al-Chalabi H, Ali T, et al. 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