WRIST FRACTURE PREDICTION USING TRANSFER LEARNING, A CASE STUDY
Main Article Content
Keywords
Wrist Fracture, Deep Learning, Transfer Learning, X-rays
Abstract
Patients regularly present to emergency rooms with suspected fractures, which frequently results in delayed treatment and poor recovery because fractures in images from X-rays are missed. Sometimes emergency care professionals who lack orthopaedic knowledge interpret these images incorrectly. Convolutional neural networks (CNNs), in particular, have attained human-level accuracy in classifying bone fractures. A deep learning model might reduce time wastage and incorrect diagnosis if it is created effectively. ResNet-101, a cutting-edge deep convolutional neural network frequently utilized in computer vision applications like object detection, image classification, and image segmentation, is used in our suggested framework, a WFP-TL model, which achieves 98.45% accuracy. ResNet-101, sporting 101 layers, regularly produces better results on benchmark datasets, MATLAB2020a is used for results and simulations. By teaching generalist medical practitioners on the front lines of healthcare, transfer learning improves patient care. The study demonstrates the potential of DL-based wrist fracture diagnosis on clinical radiographs, will serve as a foundation for future studies incorporating multi-view data for fracture classification.
References
2. Sahin, M. E. (2023). Image processing and machine learning‐based bone fracture detection and classification using X‐ray images. International Journal of Imaging Systems and Technology, 33(3), 853-865.
3. Health. Accessed: Aug. 28, 2023. [Online]. Available: https://www.hopkinsmedicine.org/health/conditions-and-diseases/fractures
4. Rundgren, J., Bojan, A., Mellstrand Navarro, C., & Enocson, A. (2020). Epidemiology, classification, treatment and mortality of distal radius fractures in adults: an observational study of 23,394 fractures from the national Swedish fracture register. BMC musculoskeletal disorders, 21(1), 1-9.
5. Guly, H. R. (2001). Diagnostic errors in an accident and emergency department. Emergency Medicine Journal, 18(4), 263-269.
6. Hand & wrist fractures Accessed: Sep 22, 2023 [Online] Available https://sportsmedicine.mayoclinic.org/condition/hand-wrist-fractures/
7. Joshi, D., & Singh, T. P. (2020). A survey of fracture detection techniques in bone X-ray images. Artificial Intelligence Review, 53(6), 4475-4517.
8. Royal College of Radiologists. (2016). Clinical radiology UK workforce census 2015 report.
9. Basha, M. A. A., Ismail, A. A. A., & Imam, A. H. F. (2018). Does radiography still have a significant diagnostic role in evaluation of acute traumatic wrist injuries? A prospective comparative study. Emergency Radiology, 25, 129-138.
10. Kohli, M., Prevedello, L. M., Filice, R. W., & Geis, J. R. (2017). Implementing machine learning in radiology practice and research. American journal of roentgenology, 208(4), 754-760.
11. Greenspan, H., Van Ginneken, B., & Summers, R. M. (2016). Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE transactions on medical imaging, 35(5), 1153-1159.
12. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
13. Russ, J. C. (2002). Image processing techniques. Encyclopedia of Imaging Science and Technology.
14. Joshi, D., & Singh, T. P. (2022, December). Novel Use of Deep Convolution Architecture Pre-Trained on Surface Crack Dataset to Localize and Segment Wrist Bone Fractures. In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 1308-1313). IEEE.
15. Reddy, K. N. K., & Cutsuridis, V. (2023, June). Deep Convolutional Neural Networks with Transfer Learning for Bone Fracture Recognition using Small Exemplar Image Datasets. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (pp. 1-5). IEEE.
16. Hendrix, N., Hendrix, W., van Dijke, K., Maresch, B., Maas, M., Bollen, S., ... & Rutten, M. (2023). Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist. European Radiology, 33(3), 1575-1588.
17. Üreten, K., Sevinç, H. F., İğdeli, U., Onay, A., & Maraş, Y. (2022). Use of deep learning methods for hand fracture detection from plain hand radiographs. Turkish Journal of Trauma and Emergency Surgery, 28(2), 196.
18. Hržić, F., Tschauner, S., Sorantin, E., & Štajduhar, I. (2022). Fracture Recognition in Paediatric Wrist Radiographs: An Object Detection Approach. Mathematics, 10(16), 2939.
19. Kim, D. H., & MacKinnon, T. (2018). Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clinical radiology, 73(5), 439-445.
20. Olczak, J., Fahlberg, N., Maki, A., Razavian, A. S., Jilert, A., Stark, A., ... & Gordon, M. (2017). Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures?. Acta orthopaedica, 88(6), 581-586.
21. Singh, A., Ardakani, A. A., Loh, H. W., Anamika, P. V., Acharya, U. R., Kamath, S., & Bhat, A. K. (2023). Automated detection of scaphoid fractures using deep neural networks in radiographs. Engineering Applications of Artificial Intelligence, 122, 106165.
22. Yoon, A. P., Lee, Y. L., Kane, R. L., Kuo, C. F., Lin, C., & Chung, K. C. (2021). Development and validation of a deep learning model using convolutional neural networks to identify scaphoid fractures in radiographs. JAMA network open, 4(5), e216096-e216096.
23. Ebsim, R., Naqvi, J., & Cootes, T. F. (2019). Automatic detection of wrist fractures from posteroanterior and lateral radiographs: a deep learning-based approach. In Computational Methods and Clinical Applications in Musculoskeletal Imaging: 6th International Workshop, MSKI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers 6 (pp. 114-125). Springer International Publishing.
24. Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., ... & Cheng, C. Y. (2020). Logistic regression was as good as machine learning for predicting major chronic diseases. Journal of clinical epidemiology, 122, 56-69.
25. Zhang, Q. (2022). A novel ResNet101 model based on dense dilated convolution for image classification. SN Applied Sciences, 4, 1-13.
26. Rao, Y., He, L., & Zhu, J. (2017, May). A residual convolutional neural network for pan-shaprening. In 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) (pp. 1-4). IEEE.
27. https://data.mendeley.com/datasets/r9bfpnvyxr/6
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Rabia Javed
Department of Computer Science, TIMES Institute, Multan
Tahir Abbas
Department of Computer Science, TIMES Institute, Multan
Jamshaid Iqbal Janjua
Al-Khawarizmi Institute of Computer Science, University of Engineering & Technology (UET), Lahore, Pakistan, & School of Computer Sciences, National College of Business Administration & Economics, Lahore
Muhammad Abubakar Muhammad
Department of Computer Science & Information Technology, Thal University Bhakkar, 30000
Sadaqat Ali Ramay
Department of Computer Science, TIMES Institute, Multan
M. Kashan Basit
Department of Computer Science, MNS-UET, Multan