{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:43:38Z","timestamp":1782233018062,"version":"3.54.5"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100023673","name":"Deanship of Scientific Research, Imam Mohammed Ibn Saud Islamic University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023673","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002713","name":"Imam Mohammed Ibn Saud Islamic University","doi-asserted-by":"publisher","award":["IMSIU-DDRSP-RP26"],"award-info":[{"award-number":["IMSIU-DDRSP-RP26"]}],"id":[{"id":"10.13039\/501100002713","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.eswa.2026.132260","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:13:12Z","timestamp":1774714392000},"page":"132260","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A positional transformer-based encoder-decoder network for segmentation of the gastrointestinal tract"],"prefix":"10.1016","volume":"322","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0858-0232","authenticated-orcid":false,"given":"SM Nuruzzaman","family":"Nobel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9195-5112","authenticated-orcid":false,"given":"S M Masfequier Rahman","family":"Swapno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-2214","authenticated-orcid":false,"given":"AKM","family":"Azad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9907-3906","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132260_bib0001","series-title":"2023 international conference on electrical, computer and communication engineering (ECCE)","first-page":"1","article-title":"An effective deep cnn model for multiclass brain tumor detection using mri images and shap explainability","author":"Ahmed","year":"2023"},{"key":"10.1016\/j.eswa.2026.132260_bib0002","first-page":"25","article-title":"Self-supervised multimodal versatile networks","volume":"33","author":"Alayrac","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132260_bib0003","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2021). Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv: 2105.05537."},{"key":"10.1016\/j.eswa.2026.132260_bib0004","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021). TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv: 2102.04306."},{"key":"10.1016\/j.eswa.2026.132260_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.132260_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106847","article-title":"Attentional deep learning based efficient AGSDCLR unet model for gastrointestinal tract segmentation","volume":"100","author":"Devi","year":"2025","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.132260_bib0007","series-title":"Iclr","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"issue":"3","key":"10.1016\/j.eswa.2026.132260_bib0008","first-page":"309","article-title":"Multi-scale hybrid network for efficient gastrointestinal organ segmentation","volume":"12","author":"Haque","year":"2024","journal-title":"Bioengineering"},{"issue":"3","key":"10.1016\/j.eswa.2026.132260_bib0009","first-page":"309","article-title":"Multi-scale hybrid network for efficient gastrointestinal organ segmentation","volume":"12","author":"Haque","year":"2024","journal-title":"Bioengineering"},{"issue":"18","key":"10.1016\/j.eswa.2026.132260_bib0010","doi-asserted-by":"crossref","first-page":"8092","DOI":"10.3390\/su16188092","article-title":"Enhancing smart grid sustainability: Using advanced hybrid machine learning techniques while considering multiple influencing factors for imputing missing electric load data","volume":"16","author":"Hou","year":"2024","journal-title":"Sustainability"},{"issue":"12","key":"10.1016\/j.eswa.2026.132260_bib0011","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.3390\/electronics14122329","article-title":"Machine learning innovations in renewable energy systems with integrated NRBO-TXAD for enhanced wind speed forecasting accuracy","volume":"14","author":"Hou","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.eswa.2026.132260_bib0012","article-title":"BiFTransnet: A bifusion transformer network for GI tract MRI segmentation","volume":"164","author":"Jiang","year":"2023","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2026.132260_bib0013","article-title":"Efficientnetb7-based architecture for accurate GI tract segmentation","volume":"101","author":"John","year":"2023","journal-title":"Computers in Medical Imaging and Graphics"},{"key":"10.1016\/j.eswa.2026.132260_bib0014","doi-asserted-by":"crossref","first-page":"15929","DOI":"10.1007\/s00521-019-04514-0","article-title":"An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection","volume":"32","author":"Khan","year":"2020","journal-title":"Neural Computing and Applications"},{"issue":"6","key":"10.1016\/j.eswa.2026.132260_bib0015","doi-asserted-by":"crossref","first-page":"e407","DOI":"10.1016\/S2589-7500(24)00063-3","article-title":"Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: A modelling study of hospital data","volume":"6","author":"Lehmann","year":"2024","journal-title":"The Lancet Digital Health"},{"issue":"5","key":"10.1016\/j.eswa.2026.132260_bib0016","doi-asserted-by":"crossref","first-page":"402","DOI":"10.3390\/e26050402","article-title":"Detracking autoencoding conditional generative adversarial network: Improved generative adversarial network method for tabular missing value imputation","volume":"26","author":"Liu","year":"2024","journal-title":"Entropy"},{"key":"10.1016\/j.eswa.2026.132260_bib0017","series-title":"2023 IEEE 3rd international conference on data science and computer application (ICDSCA)","first-page":"1337","article-title":"Establishment of second-hand sailboats price prediction model based on random forest and exploration of influencing factors","author":"Liu","year":"2023"},{"issue":"11","key":"10.1016\/j.eswa.2026.132260_bib0018","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.3390\/math13111785","article-title":"Mathematical and machine learning innovations for power systems: Predicting transformer oil temperature with beluga whale optimization-based hybrid neural networks","volume":"13","author":"Liu","year":"2025","journal-title":"Mathematics"},{"key":"10.1016\/j.eswa.2026.132260_bib0019","unstructured":"Liu, X., Wang, X., & Zhuang, J. (2022). Multi-stage deep learning for gastrointestinal tract segmentation. https:\/\/arxiv.org\/pdf\/2206.11048.pdf."},{"key":"10.1016\/j.eswa.2026.132260_bib0020","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.eswa.2026.132260_bib0021","article-title":"Deep learning in gastrointestinal lesion diagnosis using endoscopic images: A review","volume":"143","author":"Ma","year":"2022","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2026.132260_bib0022","series-title":"2016 fourth international conference on 3d vision (3DV)","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.eswa.2026.132260_bib0023","series-title":"2025 emerging technologies for intelligent systems (ETIS)","first-page":"1","article-title":"Advancing gastrointestinal tract cancer treatment: A comparative study of state-of-the-art deep learning models for MRI segmentation","author":"Neware","year":"2025"},{"key":"10.1016\/j.eswa.2026.132260_bib0024","series-title":"Proceedings of the 2023 international symposium on biomedical imaging","article-title":"Residual attention UNet for GI tract segmentation with channel fusion","author":"Niu","year":"2023"},{"key":"10.1016\/j.eswa.2026.132260_bib0025","article-title":"ResECA-unet: Enhancement of GI tract segmentation using an improved u-net framework","author":"Nobel","year":"2024","journal-title":"Preprints"},{"key":"10.1016\/j.eswa.2026.132260_bib0026","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.aiia.2024.10.005","article-title":"Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases","volume":"14","author":"Nobel","year":"2024","journal-title":"Artificial Intelligence in Agriculture"},{"issue":"6","key":"10.1016\/j.eswa.2026.132260_bib0027","doi-asserted-by":"crossref","first-page":"298","DOI":"10.3390\/info15060298","article-title":"Unmasking banking fraud: Unleashing the power of machine learning and explainable AI (XAI) on imbalanced data","volume":"15","author":"Nobel","year":"2024","journal-title":"Information"},{"key":"10.1016\/j.eswa.2026.132260_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110339","article-title":"Cancernet: A comprehensive deep learning framework for precise and intelligible cancer identification","volume":"193","author":"Nobel","year":"2025","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2026.132260_bib0029","article-title":"A novel mixed convolution transformer model for the fast and accurate diagnosis of glioma subtypes","volume":"7","author":"Nobel","year":"2024","journal-title":"Advanced Intelligent Systems"},{"issue":"5","key":"10.1016\/j.eswa.2026.132260_bib0030","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202400566","article-title":"A novel mixed convolution transformer model for the fast and accurate diagnosis of glioma subtypes","volume":"7","author":"Nobel","year":"2025","journal-title":"Advanced Intelligent Systems"},{"issue":"1","key":"10.1016\/j.eswa.2026.132260_bib0031","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-64987-5","article-title":"A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method","volume":"14","author":"Nobel","year":"2024","journal-title":"Scientific Reports"},{"issue":"6","key":"10.1016\/j.eswa.2026.132260_bib0032","doi-asserted-by":"crossref","first-page":"4452","DOI":"10.1109\/JBHI.2025.3543028","article-title":"Crt: A convolutional recurrent transformer for automatic sleep state detection","volume":"29","author":"Nobel","year":"2025","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2026.132260_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.113470","article-title":"An explainable deep learning model for mulberry leaf classification and disease detection","volume":"165","author":"Nobel","year":"2026","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132260_bib0034","series-title":"2023 26th international conference on computer and information technology (ICCIT)","first-page":"1","article-title":"Next word prediction in bangla using hybrid approach","author":"Nobel","year":"2023"},{"key":"10.1016\/j.eswa.2026.132260_bib0035","series-title":"International conference on medical imagecomputing and computer-assisted intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.eswa.2026.132260_bib0036","series-title":"Miccai","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"3","key":"10.1016\/j.eswa.2026.132260_bib0037","article-title":"Umobilenetv2 for gastrointestinal tract organ segmentation","volume":"19","author":"Sharma","year":"2024","journal-title":"PLOS One"},{"issue":"3","key":"10.1016\/j.eswa.2026.132260_bib0038","doi-asserted-by":"crossref","first-page":"309","DOI":"10.3390\/bioengineering12030309","article-title":"Encoder\u2013decoder variant analysis for semantic segmentation of gastrointestinal tract using UW-madison dataset","volume":"12","author":"Sharma","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.eswa.2026.132260_bib0039","series-title":"Naacl-hlt","article-title":"Self-attention with relative position representations","author":"Shaw","year":"2018"},{"key":"10.1016\/j.eswa.2026.132260_bib0040","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annual Review of Biomedical Engineering"},{"key":"10.1016\/j.eswa.2026.132260_bib0041","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","article-title":"Metrics for evaluating 3d medical image segmentation: Analysis, selection, and tool","volume":"15","author":"Taha","year":"2015","journal-title":"BMC Medical Imaging"},{"key":"10.1016\/j.eswa.2026.132260_bib0042","series-title":"Advances in neural information processing systems","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017"},{"issue":"2","key":"10.1016\/j.eswa.2026.132260_bib0043","first-page":"445","article-title":"Deep learning in gastrointestinal endoscopy: a review","volume":"7","author":"Yu","year":"2021","journal-title":"BMJ Innovations"},{"key":"10.1016\/j.eswa.2026.132260_bib0044","article-title":"2.5d UNet with se-resnet50 for gastrointestinal tract segmentation in MRI","volume":"84","author":"Zhou","year":"2023","journal-title":"Medical Image Analysis"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426011735?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426011735?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T00:09:07Z","timestamp":1781309347000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426011735"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":44,"alternative-id":["S0957417426011735"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132260","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A positional transformer-based encoder-decoder network for segmentation of the gastrointestinal tract","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132260","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"132260"}}