{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T21:29:32Z","timestamp":1777757372386,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Robotics Computer Surgery"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early and accurate pancreatic cancer (PC) detection remains a major clinical challenge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce a novel hybrid deep learning framework for automated classification of CT images, which requires fewer computational resources while achieving high diagnostic performance. We integrated a lightweight MobileNetV3Small backbone with a convolutional block attention module and Low\u2010rank Attention with Shared Efficient Representations (LASER) to enhance feature representation. Feature maps are projected via a 1\u00a0\u00d7\u00a01 convolution into token sequences and processed through a transformer encoder to capture long\u2010range dependencies. A parallel global average pooling extracts aggregated features, fused using a cross\u2010type interaction (CTI) module.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model was evaluated on 18,942 CT images and achieved 99.34% accuracy, AUC\u2010ROC of 0.9996, Cohen's Kappa of 0.9897, and MCC of 0.9859, outperforming ResNet50, EfficientNetB0, and ViT variants with only 1.26 million parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Explainability analyses using Grad\u2010CAM, Grad\u2010CAM++, and attention visualisation suggest that the model focuses on clinically relevant regions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/rcs.70164","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T13:39:20Z","timestamp":1776519560000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Lightweight Model Using Low\u2010Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis"],"prefix":"10.1002","volume":"22","author":[{"given":"Vishesh","family":"Tanwar","sequence":"first","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology Chitkara University  Rajpura Punjab India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3400-3504","authenticated-orcid":false,"given":"Bhisham","family":"Sharma","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology Chitkara University  Rajpura Punjab India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhirendra Prasad","family":"Yadav","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering &amp; Applications G.L.A. University  Mathura Uttar Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5490-6030","authenticated-orcid":false,"given":"Panos","family":"Liatsis","sequence":"additional","affiliation":[{"name":"Department of Computer Science Center for Cyber Physical Systems Khalifa University  Abu Dhabi UAE"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"e_1_2_10_2_1","article-title":"The Value of Artificial Intelligence Techniques in Predicting Pancreatic Ductal Adenocarcinoma With EUS Images: A Meta\u2010Analysis and Systematic Review","author":"Yin H.","year":"2022","journal-title":"Endoscopic Ultrasound"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0307900"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/00051144.2023.2213439"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2023.0140307"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911\u2010025\u201002923\u2010x"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261\u2010024\u201004644\u20107"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CICN.2015.72"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.4103\/2303\u20109027.180473"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_60"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v26.i34.5232"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/jhbp.825"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9176172"},{"key":"e_1_2_10_14_1","first-page":"878","article-title":"Pancreatic Cancer Grading in Pathological Images Using Deep Learning Convolutional Neural Networks","volume":"10","author":"Sehmi M. 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