SBRF-Det: Scale-Boundary Representative Fusion for Multi-Finding Detection in Panoramic Dental Radiographs.
Authors
Affiliations (8)
Affiliations (8)
- Department of Prosthetic Dentistry, Faculty of Dentistry, Igdir University, Igdir 76000, Turkey.
- Department of Prosthetic Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey.
- Department of Computer Engineering, Faculty of Engineering and Architecture, Bitlis Eren University, Bitlis 13000, Turkey.
- Department of Health Information Management and Technology, College of Applied Medical Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Istanbul Topkapi University, Istanbul 34000, Turkey.
- Department of Computer Engineering, Faculty of Engineering, Igdir University, Igdir 76000, Turkey.
- Department of Electronics and Information Technologies, Faculty of Architecture and Engineering, Nakhchivan State University, Nakhchivan AZ 7012, Azerbaijan.
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Fenerbahce University, Istanbul 34758, Turkey.
Abstract
<b>Background</b>: Panoramic dental radiographs frequently contain multiple coexisting pathological, anatomical, and treatment-related findings that vary in scale, contrast, and boundary definition. Although recent one-stage detectors provide strong performance, reliable localization of small or weakly defined findings remains challenging. <b>Methods</b>: We propose SBRF-Det, a one-stage multi-finding detector integrating scale-dependent representative allocation with boundary-conditioned local-global feature fusion at P4/16. The method was evaluated on OralXrays-9, comprising 12,688 panoramic radiographs and 84,099 retained annotations across nine categories, using a fixed 2688-image hold-out cohort. Twenty-two detector configurations were compared under a common protocol, and A0-A2 were further assessed using three independent seeds and paired radiograph-level bootstrap analysis. <b>Results</b>: SBRF-Det achieved 0.8777 precision, 0.8979 recall, 0.9152 mAP50, and 0.7029 mAP50-95. Relative to the matched YOLO12l baseline, mAP50-95 increased by 0.95 percentage points. Performance remained class dependent, with apical periodontitis reaching 0.4526 mAP50-95 versus 0.8401-0.8842 for implants, porcelain crowns, and ceramic bridges. SBRF-Det used 93.06 GFLOPs versus 95.99 for A0, but inference latency increased from 5.946 to 8.851 ms/image. Three-seed experiments preserved the A0-A1-A2 ordering, while paired bootstrap intervals for A2-A0 and A2-A1 excluded zero. <b>Conclusions</b>: SBRF-Det improves strict localization through representative and boundary-aware feature modeling, but the gain is modest and introduces a runtime trade-off. Patient-linked, multi-center, and reader-centered validation remains necessary before clinical deployment.