{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:25:09Z","timestamp":1783023909703,"version":"3.54.6"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110536","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:27:30Z","timestamp":1780360050000},"page":"110536","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DeepPanNet: Robust pancreatic cancer segmentation and classification using wavelet-enhanced capsular hierarchies and osprey optimization algorithm"],"prefix":"10.1016","volume":"125","author":[{"given":"M.","family":"Deeparani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Ramalingam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Suresh Babu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Murugesan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110536_b0005","article-title":"Automated detection of pancreatic cancer with segmentation and classification using fusion of UNET and CNN through spider monkey optimization","volume":"102","author":"Chaithanyadas","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110536_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106657","article-title":"MFCNet: a multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images","volume":"155","author":"Wang","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110536_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107844","article-title":"Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies","volume":"169","author":"Chen","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110536_b0020","doi-asserted-by":"crossref","first-page":"3118","DOI":"10.1016\/j.procs.2024.04.295","article-title":"Deep learning techniques for pancreatic cancer analysis: a systematic review and implantation prerequisites","volume":"235","author":"Chhikara","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.bspc.2026.110536_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106294","article-title":"Deep causal learning for pancreatic cancer segmentation in CT sequences","volume":"175","author":"Li","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.bspc.2026.110536_b0030","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.neucom.2022.10.060","article-title":"TD-Net: trans-deformer network for automatic pancreas segmentation","volume":"517","author":"Dai","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.bspc.2026.110536_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102919","article-title":"SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images","volume":"154","author":"Li","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bspc.2026.110536_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102753","article-title":"A dual-transformation with contrastive learning framework for lymph node metastasis prediction in pancreatic cancer","volume":"85","author":"Chen","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110536_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104170","article-title":"Multi-target segmentation of pancreas and pancreatic tumor based on fusion of attention mechanism","volume":"79","author":"Cao","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110536_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104173","article-title":"RTUNet: residual transformer UNet specifically for pancreas segmentation","volume":"79","author":"Qiu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110536_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104516","article-title":"A dual branch and fine-grained enhancement network for pancreatic tumor segmentation in contrast enhanced CT images","volume":"82","author":"Zhou","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110536_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104583","article-title":"Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network","volume":"83","author":"Yao","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"issue":"13","key":"10.1016\/j.bspc.2026.110536_b0065","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.3390\/cancers16132403","article-title":"Improved pancreatic cancer detection and localization on CT scans: a computer-aided detection model utilizing secondary features","volume":"16","author":"Ramaekers","year":"2024","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.bspc.2026.110536_b0070","first-page":"1","article-title":"Machine learning via DARTS-Optimized MobileViT models for pancreatic cancer diagnosis with graph-based deep learning","volume":"25","author":"Alaca","year":"2025","journal-title":"BMC Med. Inf. Decis. Making"},{"issue":"1","key":"10.1016\/j.bspc.2026.110536_b0075","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41747-023-00419-9","article-title":"Artificial intelligence for assessment of vascular involvement and tumorresectability on CT in patients with pancreatic cancer","volume":"8","author":"Bereska","year":"2024","journal-title":"Eur. Radio. Exp."},{"key":"10.1016\/j.bspc.2026.110536_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121064","article-title":"LMNS-Net: lightweight multiscale novel semantic-net deep learning approach used for automatic pancreas image segmentation in CT scan images","volume":"234","author":"Paithane","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110536_b0085","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.neunet.2022.10.026","article-title":"Temperature guided network for 3D joint segmentation of the pancreas and tumors","volume":"157","author":"Li","year":"2023","journal-title":"Neural Netw."},{"key":"10.1016\/j.bspc.2026.110536_b0090","unstructured":"Babaei, Reza, Samuel Cheng, Theresa Thai, and Shangqing Zhao. \u201cPancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models.\u201d arXiv preprint arXiv:2406.02653 (2024)."},{"key":"10.1016\/j.bspc.2026.110536_b0095","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2024.1362850","article-title":"Weakly supervised large-scale pancreatic cancer detection using multi-instance learning","volume":"14","author":"Mandal","year":"2024","journal-title":"Front. Oncol."},{"key":"10.1016\/j.bspc.2026.110536_b0100","doi-asserted-by":"crossref","unstructured":"Moglia, Andrea, Elia Clement Nastasio, Luca Mainardi, and Pietro Cerveri. \u201cMiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and Detection.\u201d arXiv preprint arXiv:2412.15925 (2024).","DOI":"10.1007\/s41666-025-00224-6"},{"key":"10.1016\/j.bspc.2026.110536_b0105","doi-asserted-by":"crossref","unstructured":"Seo, Kangwon, Jung Hyun Lim, Jin-Seok Park, Min Jae Yang, Tae Jun Song, and Suhyun Park. \u201cDiagnosis of invasive pancreatic cancer in endoscopic ultrasound images leveraging translation models.\u201d Biomedical Signal Processing and Control 102 (2025): 107389.","DOI":"10.1016\/j.bspc.2024.107389"},{"key":"10.1016\/j.bspc.2026.110536_b0110","doi-asserted-by":"crossref","unstructured":"Li, Wenqi, Yingli Chen, Keyang Zhou, Xiaoxiao Hu, Zilu Zheng, Yue Yan, Xinpeng Zhang, Wei Tang, and Zhenxing Qian. \u201cAn Exceptional Dataset For Rare Pancreatic Tumor Segmentation.\u201d arXiv preprint arXiv:2501.17555 (2025).","DOI":"10.1109\/ICASSP49660.2025.10888474"},{"issue":"4","key":"10.1016\/j.bspc.2026.110536_b0115","doi-asserted-by":"crossref","first-page":"438","DOI":"10.3390\/diagnostics14040438","article-title":"Denisa Andreea Anghel, Gina-Ionela Rusu-Munteanu, Radu Lucian Dumitru, and Ioana Gabriela Lupescu. \u201cPancreatic adenocarcinoma: imaging modalities and the role of artificial intelligence in analyzing CT and MRI images.\u201d","volume":"14","author":"Anghel","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2026.110536_b0120","doi-asserted-by":"crossref","unstructured":"Zheng, Zhilin, Xu Fang, Jiawen Yao, Mengmeng Zhu, Le Lu, Yu Shi, Hong Lu et al. \u201cDeep Attention Learning for Pre-operative Lymph Node Metastasis Prediction in Pancreatic Cancer via Multi-object Relationship Modeling.\u201d International Journal of Computer Vision (2024): 1-24.","DOI":"10.1007\/s11263-024-02314-1"},{"key":"10.1016\/j.bspc.2026.110536_b0125","article-title":"Pancreatic cancer segmentation and classification in CT imaging using antlion optimization and deep learning mechanism","volume":"14","author":"Khdhir","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.bspc.2026.110536_b0130","first-page":"1","article-title":"DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging","author":"Thanya","year":"2025","journal-title":"Abdominal Radiol."},{"issue":"3","key":"10.1016\/j.bspc.2026.110536_b0135","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1007\/s00521-024-10521-7","article-title":"Integrating expert guidance with gradual moment approximation (GMAp)-enhanced transfer learning for improved pancreatic cancer classification","volume":"37","author":"Chhikara","year":"2025","journal-title":"Neural Comput. & Applic."},{"key":"10.1016\/j.bspc.2026.110536_b0140","first-page":"1","article-title":"A novel hybrid optimization-based improved artificial intelligence methods for pancreatic disease segmentation and diagnosis","author":"Yugandhar","year":"2024","journal-title":"Multimed. Tools Appl."},{"issue":"2","key":"10.1016\/j.bspc.2026.110536_b0145","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s42600-024-00352-9","article-title":"IANFIS: a machine learning\u2013based optimized technique for the classification and segmentation of pancreatic cancer","volume":"40","author":"Dodda","year":"2024","journal-title":"Res. Biomed. Eng."},{"issue":"10","key":"10.1016\/j.bspc.2026.110536_b0150","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ad3cb1","article-title":"Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions","volume":"69","author":"Zhang","year":"2024","journal-title":"Phys. Med. Biol."},{"issue":"12","key":"10.1016\/j.bspc.2026.110536_b0155","doi-asserted-by":"crossref","first-page":"10489","DOI":"10.1007\/s11042-024-19318-1","article-title":"Stroke detection in the brain using MRI and deep learning models","volume":"84","author":"Polamuri","year":"2025","journal-title":"Multimed. Tools Appl."},{"issue":"4","key":"10.1016\/j.bspc.2026.110536_b0160","first-page":"663","article-title":"A novel deep learning model for medical image segmentation with convolutional neural network and transformer","volume":"15","author":"Zhang","year":"2023","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"key":"10.1016\/j.bspc.2026.110536_b0165","unstructured":"NIH Dataset - https:\/\/www.cancerimagingarchive.net\/collection\/pancreas-ct\/."},{"key":"10.1016\/j.bspc.2026.110536_b0170","doi-asserted-by":"crossref","unstructured":"Antonelli, Michela, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A. Landman, Geert Litjens et al. \u201cThe medical segmentation decathlon.\u201d Nature communications 13, no. 1 (2022): 4128.","DOI":"10.1038\/s41467-022-30695-9"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426010906?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426010906?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:58:04Z","timestamp":1783022284000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426010906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":34,"alternative-id":["S1746809426010906"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110536","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DeepPanNet: Robust pancreatic cancer segmentation and classification using wavelet-enhanced capsular hierarchies and osprey optimization algorithm","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110536","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110536"}}