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Swati Andhale,
Keshav Pradip Bave,
Pavan Gulabrao Wakale,
- Assistant Professor,, Department of Master of Computer Application, Genba Sopanrao Moze Trust Parvatibai Genba Moze College of Engineering, Wagholi, Pune,, Maharashtra,, India
- Student,, Department of Master of Computer Application, Genba Sopanrao Moze Trust Parvatibai Genba Moze College of Engineering, Wagholi, Pune,, Maharashtra,, India
- Student,, Department of Master of Computer Application, Genba Sopanrao Moze Trust Parvatibai Genba Moze College of Engineering, Wagholi, Pune,, Maharashtra,, India
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Bone fracture detection is a critical aspect of medical diagnostics, traditionally relying on manual interpretation of radiographic images by experienced radiologists. This discipline has undergone a revolution with the introduction of machine learning (ML), which can improve accuracy, shorten diagnosis times, and lessen human error. This study investigates the use of different machine learning methods to enhance and automate the identification of bone fractures in radiography pictures. We utilized a dataset comprising thousands of labeled X-ray images, preprocessed to enhance feature extraction. Because Convolutional Neural Networks (CNNs) are good at picture recognition, they were used. To guarantee a reliable assessment of performance, the models were trained, verified, and tested on distinct subsets. Our findings demonstrate that ML models, particularly deep learning architectures, can achieve high accuracy, sensitivity, and specificity in fracture detection, outperforming traditional methods. Furthermore, the integration structures. of ML-based systems in clinical workflows can assist radiologists by providing a reliable second opinion, ultimately improving patient outcomes. This paper discusses methodology, including data preparation, model selection, training processes, and performance metrics, highlighting the potential and challenges of deploying ML for bone fracture detection in real-world medical settings.
Keywords: Bone fracture, ML, medical, CNNs, Deep Learning, X-ray images etc.
[This article belongs to International Journal of Optical Innovations & Research (ijoir)]
Swati Andhale, Keshav Pradip Bave, Pavan Gulabrao Wakale. Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics. International Journal of Optical Innovations & Research. 2024; 02(02):1-10.
Swati Andhale, Keshav Pradip Bave, Pavan Gulabrao Wakale. Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics. International Journal of Optical Innovations & Research. 2024; 02(02):1-10. Available from: https://journals.stmjournals.com/ijoir/article=2024/view=0
References
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[1]. Yadav DP, Rathor S. Bone fracture detection and classification using deep learning approach. In2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) 2020 Feb 28 (pp. 282-285). IEEE. [2]. Khatik I, Kadam S. A systematic review of bone fracture detection models using convolutional neural network approach. Journal of Pharmaceutical Negative Results. 2022 Nov 7:153-8. [3]. Achawale MA, More Y, Bankar MA, Ghuge MP, Zole MG. Bone Fracture Detection Using Convolutional Neural Network (Cnn). Journal of Basic Science and Engineering. 2024 Jan 3;21(1):323-33. [4]. Mohanty S, Senapati MR. Fracture detection from X-ray images using different Machine Learning Techniques. In2023 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS) 2023 Sep 1 (pp. 1-6). IEEE. [5]. Thian YL, Li Y, Jagmohan P, Sia D, Chan VE, Tan RT. Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiology: Artificial Intelligence. 2019 Jan 30;1(1): e180001. [6]. Rajpurkar P, Beam AL, Manrai AK. Pixels and Pitfalls: Building Robust Artificial Intelligence for Medical Imaging. NEJM AI. 2024 Sep 26;1(10): AIp2400803. [7]. Kutbi M. Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review. Diagnostics. 2024 Aug 27;14(17):1879. [8]. Argall BD, Chernova S, Veloso M, Browning B. A survey of robot learning from demonstration. Robotics and autonomous systems. 2009 May 31;57(5):469-83. [9]. Swapna N, Malge R. Classification and detection of bone fracture using machine learning. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET). 2022. [10]. Meena T, Roy S. Bone fracture detection using deep supervised learning from radiological images: A paradigm shift. Diagnostics. 2022 Oct 7;12(10):2420
| Volume | 02 |
| Issue | 02 |
| Received | 09/10/2024 |
| Accepted | 13/11/2024 |
| Published | 26/11/2024 |