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Intell."],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:p> To address the problem of missed diagnosis in rib fracture detection from CT scans, this study introduces an enhanced model, called Faster-RCNN-SE-FA, which is built upon the traditional Faster-RCNN architecture. The proposed model integrates a novel filter anchor method and thoroughly considers the specific imaging characteristics of ribs in CT images. The preprocessing of the image is followed by applying the Squeeze-and-Excitation (SE) module, which enhances the discrimination of features in the channel dimension while preserving the location Sensitivity (Sen) important for target detection tasks. Consequently, this modification leads to a significant improvement in model performance. Empirical experiments, conducted on CT sequences of 130 rib feature cases provided by the First Affiliated Hospital of Ningbo University, demonstrate that the Faster-RCNN-SE-FA model achieves better Sen and accuracy compared to traditional methods, including the baseline Faster-RCNN. <\/jats:p>","DOI":"10.1142\/s0218001424520256","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:56:27Z","timestamp":1726221387000},"source":"Crossref","is-referenced-by-count":2,"title":["Rib Fracture Detection Model Based on Faster-RCNN-SE-FA Algorithm"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3834-8123","authenticated-orcid":false,"given":"Xiuchao","family":"He","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Ningbo University, 59 Liuting Street, Ningbo 315010, \u00a0P.\u00a0R. 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