Bone Fracture Detection through Advanced Neural Network Architectures

Open Access

Issue		E3S Web Conf. Volume 619, 2025 3^rd International Conference on Sustainable Green Energy Technologies (ICSGET 2025)


Article Number		03011
Number of page(s)		11
Section		Smart Electronics for Sustainable Solutions
DOI		https://doi.org/10.1051/e3sconf/202561903011
Published online		12 March 2025

S. C. Medaramatla, C. V. Samhitha, S. D. Pande and S. R. Vinta, “Detection of Hand Bone Fractures in X-Ray Images Using Hybrid YOLO NAS,” in IEEE Access, vol. 12, pp. 57661-57673, 2024, doi: 10.1109/ACCESS.2024 [CrossRef] [Google Scholar]
M. Yaseen, M. Ali, S. Ali, A. Hussain, M. -I. Joo and H. -C. Kim, “Cervical Spine Fracture Detection and Classification Using Two-Stage Deep Learning Methodology,” in IEEE Access, vol. 12, pp. 72131-72142, 2024 [CrossRef] [Google Scholar]
K. -H. Choi and J. -E. Ha, “Object Detection Method Using Image and Number of Objects on Image as Label,” in IEEE Access, vol. 12, pp. 121915-121931, 2024 [CrossRef] [Google Scholar]
A. Naseer et al., “CNN-Based Object Detection via Segmentation Capabilities in Outdoor Natural Scenes,” in IEEE Access, vol. 12, pp. 84984-85000, 2024 [CrossRef] [Google Scholar]
D. Wang, Y. Xia and Z. Yu, “Feature-Based Interpretation of Image Classification With the Use of Convolutional Neural Networks,” in IEEE Access, vol. 12, pp. 70377-70391, 2024 [CrossRef] [Google Scholar]
H. Liang, H. Wang, L. Mao, R. Liu, Z. Wang and K. Wang, “Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network,” in IEEE Access, vol. 11, pp. 94057-94068, 2023 [CrossRef] [Google Scholar]
J. G. Wolynski, M. M. Ilić, B. M. Notaroš, K. M. Labus, C. M. Puttlitz and K. C. Mcgilvray, “Vivaldi Antennas for Contactless Sensing of Implant Deflections and Stiffness for Orthopaedic Applications,” in IEEE Access, vol. 10, pp. 1151-1161, 2022 [CrossRef] [PubMed] [Google Scholar]
H. Salehinejad et al., “Deep Sequential Learning For Cervical Spine Fracture Detection On Computed Tomography Imaging,” 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, pp. 1911-1914,2021 [Google Scholar]
R. Girshick, J. Donahue, T. Darrell and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580-587, 2014 [Google Scholar]
C. Guan, K. K. F. Yuen and Q. Chen, “Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation,” 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, UK, pp. 396-399,2017 [Google Scholar]
Hariharan, Shanmugasundaram, D. Anandan, Murugaperumal Krishnamoorthy, Vinay Kukreja, Nitin Goyal, and Shih-Yu Chen. “Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models.” CMES- Computer Modeling in Engineering & Sciences 142, no. 1 (2025). [Google Scholar]
G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger, “Densely Connected Convolutional Networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 2261-2269,2017 [Google Scholar]
F. Mohammad, S. Al-Ahmadi and J. Al-Muhtadi, “Block-Deep: A Hybrid Secure Data Storage and Diagnosis Model for Bone Fracture Identification of Athlete From X-Ray and MRI Images,” in IEEE Access, vol. 11, pp. 142360-142370, 2023 [CrossRef] [Google Scholar]
M. A. Arefeen, S. T. Nimi and M. S. Rahman, “Neural network-based undersampling techniques”, IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 2, pp. 1111-1120, Feb. 2022. [CrossRef] [Google Scholar]
A. Chattopadhay, A. Sarkar, P. Howlader and V. N. Balasubramanian, “Grad- CAM++: Generalized gradient-based visual explanations for deep convolutional networks”, Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), pp. 839-847, Mar. 2018. [Google Scholar]
K. C. Santos, C. A. Fernandes and J. R. Costa, “Feasibility of bone fracture detection using microwave imaging”, IEEE Open J. Antennas Propag., vol. 3, pp. 836-847, 2022. [CrossRef] [Google Scholar]
Z. N. Haitaamar and N. Abdulaziz, “Detection and Semantic Segmentation of Rib Fractures using a Convolutional Neural Network Approach,” 2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Korea, Republic of, pp. 1-4, 2021. [Google Scholar]

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