Amiya, G., Murugan, P.R., Ramaraj, K., et al.: LMGU-NET: methodological intervention for prediction of bone health for clinical recommendations. J. Supercomput. 80, 15636–15663 (2024). https://doi.org/10.1007/s11227-024-06048-2

Article  Google Scholar 

Açıcı, K., Sümer, E., Beyaz, S.: Comparison of different machine learning approaches to detect femoral neck fractures in x-ray images. Health Technol. 11, 643–653 (2021). https://doi.org/10.1007/s12553-021-00543-9

Article  Google Scholar 

Anu, T.C., Raman, R.: Detection of bone fracture using image processing methods. Int. J. Comput. Appl. 975, 8887 (2015)

Google Scholar 

Cheng, C.T., Ho, T.Y., Lee, T.Y., Chang, C.C., Chou, C.C., Chen, C.C., Chung, I., Liao, C.H.: Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur. Radiol. 29, 5469–5477 (2019). https://doi.org/10.1007/s00330-019-06167-y

Article  PubMed  PubMed Central  Google Scholar 

Dlshad Ahmed, K., Hawezi, R.: Detection of bone fracture based on machine learning techniques. Measurement Sens. (2023). https://doi.org/10.1016/j.measen.2023.100723

Article  Google Scholar 

Dimililer, K.: IBFDS: intelligent bone fracture detection system. Proced. Comput. Sci. 120, 260–267 (2017). https://doi.org/10.1016/j.procs.2017.11.237

Article  Google Scholar 

Fischer, S., Kapinos, K.A., Mulcahy, A., Pinto, L., Hayden, O., Barron, R.: Estimating the long-term functional burden of osteoporosis-related fractures. Osteoporos Int. 28(10), 2843–2851 (2017)

Article  CAS  PubMed  Google Scholar 

Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016)

Article  PubMed  Google Scholar 

Khatik, I., Kadam, S.: A systematic review of bone fracture detection models using convolutional neural network approach. J Pharm. Negat. Results (2022). https://doi.org/10.47750/pnr.2022.13.s09.019

Article  Google Scholar 

Ju, R.-Y., & Cai, W. Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm. Retrieved from http://arxiv.org/abs/2304.05071 (2023)

Krupinski, E.A., Berbaum, K.S., Caldwell, R.T., Schartz, K.M., Kim, J.: Long radiology workdays reduce detection and accommodation accuracy. J. Am. Coll. Radiol. 7, 698–704 (2010)

Article  PubMed  PubMed Central  Google Scholar 

Khatik, I.: A study of various bone fracture detection techniques. Int. J. Eng. Comput. Sci. (2017). https://doi.org/10.18535/ijecs/v6i5.38

Article  Google Scholar 

Karanam, S.R., Srinivas, Y., Chakravarty, S.: A supervised approach to musculoskeletal imaging fracture detection and classification using deep learning algorithms. Comput. Assist. Method. Eng. Sci. 30(3), 269–285 (2023). https://doi.org/10.24423/cames.682

Article  Google Scholar 

Kishor, K.: Using a half cheetah habitat for random augmentation computing. Multimed. Tools Appl. (2024). https://doi.org/10.1007/s11042-024-19084-0

Article  Google Scholar 

Kishor, K., Shukla, A., Thakur, A. (2024). Vehicle Classification and License Number Plate Detection Using Deep Learning. In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering. ICMETE 2023. Lecture Notes in Networks and Systems, vol 894. (2024) Springer, Singapore. https://doi.org/10.1007/978-981-99-9562-2_5

Kim, D.H., MacKinnon, T.: Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin. Radiol. 73, 439–445 (2018). https://doi.org/10.1016/j.crad.2017.11.015

Article  CAS  PubMed  Google Scholar 

Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., Hanel, D., Gardner, M., Gupta, A., Hotchkiss, R., et al.: Deep neural network improves fracture detection by clinicians. Proc. Natl. Acad. Sci. u.s.a. 115, 11591–11596 (2018)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lydia, L., Vijaya Kumar, K., Narasimha Rao, G., Laxmi Lydia, E., & Vijaya Kumar, D. Analysis of Advanced Deep Learning Approaches for the Multiple Bone Fracture detection. (2023) https://doi.org/10.21203/rs.3.rs-2995613/v1

Meena, T., Roy, S.: Bone fracture detection using deep supervised learning from radiological images: a paradigm shift. Diagnostics (2022). https://doi.org/10.3390/diagnostics12102420

Article  PubMed  PubMed Central  Google Scholar 

Mehta, R., Pareek, P., Jayaswal, R., Patil, S., Vyas, K.: A bone fracture detection using ai-based techniques. Scalable Comput. 24(2), 161–171 (2023). https://doi.org/10.12694/scpe.v24i2.2081

Article  Google Scholar 

Moon, G., Kim, S., Kim, W., Kim, Y., Jeong, Y., Choi, H.S.: Computer aided facial bone fracture diagnosis (CA-FBFD) system based on object detection model. IEEE Access 10, 79061–79070 (2022). https://doi.org/10.1109/ACCESS.2022.3192389

Article  Google Scholar 

Majkowska, A., Mittal, S., Steiner, D.F., Reicher, J.J., McKinney, S.M., Duggan, G.E., Eswaran, K., Chen, P.-H.C., Liu, Y., Kalidindi, S.R., et al.: Chest radiograph interpretation with deep learning models: Assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology 294, 421–431 (2020). https://doi.org/10.1148/radiol.2019191293

Article  PubMed  Google Scholar 

Oyeranmi, A., Ronke, B., Mohammed, R., Edwin, A.: Detection of fracture bones in X-ray images categorization. J. Adv. Math. Comput. Sci. (2020). https://doi.org/10.9734/jamcs/2020/v35i430265

Article  Google Scholar 

Olczak, J., Fahlberg, N., Maki, A., Razavian, A.S., Jilert, A., Stark, A., Sköldenberg, O., Gordon, M.: Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 88, 581–586 (2017). https://doi.org/10.1080/17453674.2017.1344459

Article  PubMed  PubMed Central  Google Scholar 

Peruri, S., Vamsi, J., Kattubadi, D., &Prudhvi, G. Bone fracture detection using Image Processing Radar detection using ultrasonic sensor View project Automatic street light system using IOT || Arduino and buzzer View project. Retrieved from www.ijsdr.org (2020)

Pranata, Y.D., Wang, K.C., Wang, J.C., Idram, I., Lai, J.Y., Liu, J.W., Hsieh, I.-H.: Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput. Methods Programs Biomed. 171, 27–37 (2019). https://doi.org/10.1016/j.cmpb.2019.02.006

Article  PubMed  Google Scholar 

Rayan, J.C., Reddy, N., Kan, J.H., Zhang, W., Annapragada, A.: Binomial classification of pediatric elbow fractures using a deep learning multiview approach emulating radiologist decision making. Radiol. Artif. Intell. 1, e180015 (2019). https://doi.org/10.1148/ryai.2019180015

Article  PubMed  PubMed Central  Google Scholar 

Svedbom A, Hernlund E, Ivergård M, Compston J, Cooper C, Stenmark J, EU Review Panel of IOF. Osteoporosis in the European Union: a compendium of country-specific reports. Arch Osteoporos 2013 11;8(1-2):137.

Sharma A., Jha N., Kishor K. Predict COVID-19 with Chest X-ray. In: Gupta D., Polkowski Z., Khanna A., Bhattacharyya S., Castillo O. (eds) Proceedings of Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 90. (2022) Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_16.

Santos, K.C., Fernandes, C.A., Costa, J.R.: Feasibility of bone fracture detection using microwave imaging. IEEE Open J. Ant. Propag. 3, 836–847 (2022). https://doi.org/10.1109/OJAP.2022.3194217

Article  Google Scholar 

Tomita, N., Cheung, Y.Y., Hassanpour, S.: Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput. Biol. Med. 98, 8–15 (2018). https://doi.org/10.1016/j.compbiomed.2018.05.011

Article  PubMed  Google Scholar 

Vironicka, S., &Sathiaseelan, J. G. R. (n.d.). International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Framework for Classifying Long Bone Detection Using Image Processing Techniques. In Original Research Paper International Journal of Intelligent Systems and Applications in Engineering IJISAE (Vol. 2022, Issue 1s). Retrieved from www.ijisae.org

Verma, R. K., Kishor, K., & Jha, S. K. Big Data Analytics in Bioinformatics and Healthcare. In R. Khan, I. Kumar, & P. Praveen (Eds.), Applications of Parallel Data Processing for Biomedical Imaging (pp. 25–43). IGI Global. (2024) https://doi.org/10.4018/979-8-3693-2426-4.ch002

Verma, R. K. & Kishor, K. Image Processing Applications in Agriculture With the Help of AI. In M. Khan, R. Khan, P. Praveen, A. Verma, & M. Panda (Eds.), Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0 (pp. 162–181). IGI Global. (2024) https://doi.org/10.4018/979-8-3693-0782-3.ch010.

Wang, X., Xu, Z., Tong, Y., Xia, L., Jie, B., Ding, P., Bai, H., Zhang, Y., He, Y.: Detection and classification of mandibular fracture on CT scan using deep convolutional neural network. Clin. Oral Investig. 26, 4593–4601 (2022). https://doi.org/10.1007/s00784-022-04427-8

Article  PubMed  Google Scholar 

Wah Myint, W.: Analysis on leg bone fracture detection and classification using X-ray images. Mach. Learn. Res. 3(3), 49 (2018). https://doi.org/10.11648/j.mlr.20180303.11

Article  Google Scholar 

Yadav, D.P., Sharma, A., Athithan, S., Bhola, A., Sharma, B., Dhaou, I.B.: Hybrid SFNet model for bone fracture detection and classification using ML/DL. Sensors (2022). https://doi.org/10.3390/s22155823

Article  PubMed  PubMed Central  Google Scholar 

Yadav, D. P., &Rathor, S. Bone Fracture Detection and Classification using Deep Learning Approach. 2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control, PARC 2020, 282–285. (2020) https://doi.org/10.1109/PARC49193.2020.236611

Yang, A. Y., & Cheng, L. Long-Bone Fracture Detection using Artificial Neural Networks based on Line Features of X-ray Images. Retrieved from http://arxiv.org/abs/1902.07458 (2019)

Zeelan Basha, C.M.A.K., Padmaja, T.M., Balaji, G.N.: An effective and reliable computer automated technique for bone fracture detection. EAI Endorsed Trans. Pervasive Health Technol. (2019). https://doi.org/10.4108/eai.13-7-2018.162402

Article  Google Scholar