{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:15:31Z","timestamp":1787019331142,"version":"build-2736575974"},"reference-count":41,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Therapeutic Advances in Musculoskeletal"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Bone fractures present a significant diagnostic challenge in medical imaging, necessitating accurate and automated classification methods. Recent advancements in deep learning have greatly enhanced the diagnostic precision while reducing human error.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>This study proposes an ensemble deep learning model, EnsembleAttenBoneNet, that integrates fine-tuned ResNet50 and EfficientNetB3 models augmented with a Squeeze-and-Excitation (SE) attention mechanism, for robust classification of bone fractures in X-ray images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>The dataset consists of ten distinct fracture categories, such as avulsion, comminuted, greenstick, and pathological fractures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Preprocessing techniques, including resizing, normalization, and augmentation, have been applied to improve generalization. Features extracted from both networks were concatenated and refined using the SE attention module to enhance feature representation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The proposed model achieved a classification accuracy of 99.48%, outperforming the individual models (EfficientNetB3: 98.56%, ResNet50: 97.86%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Experimental results affirm that integrating deep learning models with attention mechanisms significantly improve diagnostic accuracy, rendering the model a valuable tool for clinical fracture detection. Future research will investigate dataset extension and conduct real-world validation to enhance its usability in medical imaging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1759720x251405099","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T10:22:59Z","timestamp":1768990979000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["A transfer learning\u2013based approach for automated bone fracture classification in X-ray imaging"],"prefix":"10.1177","volume":"18","author":[{"given":"Ruchika","family":"Bhuria","sequence":"first","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheifali","family":"Gupta","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rania M.","family":"Ghoniem","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaibir","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suman","family":"Rani","sequence":"additional","affiliation":[{"name":"Department of ECE, Galgotias University, Greater Noida, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1767-4595","authenticated-orcid":false,"given":"Belayneh Matebie","family":"Taye","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Informatics, University of Gondar, Gondar 1961, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salil","family":"Bharany","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22155823"},{"issue":"20","key":"e_1_3_4_3_2","first-page":"6173","article-title":"Bone abnormalities detection and classification using deep learning-vgg16 algorithm","volume":"100","author":"Barhoom AM","year":"2022","unstructured":"Barhoom AM, Al-Hiealy MRJ, Abu-Naser SS. Bone abnormalities detection and classification using deep learning-vgg16 algorithm. J Theor Appl Inf Technol 2022; 100(20): 6173\u20136184.","journal-title":"J Theor Appl Inf Technol"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13213317"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1506686"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/OJEMB.2025.3527877"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01546-4"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2024.12.028"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15020175"},{"key":"e_1_3_4_10_2","first-page":"3","volume-title":"Smart medical imaging for diagnosis and treatment planning","author":"Chakraborty S","year":"2025","unstructured":"Chakraborty S, Misra B, Mridha MF. Enhancing intelligent medical imaging to revolutionize healthcare. 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