Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics

Year : 2024 | Volume : 02 | Issue : 02 | Page : 17 22
    By

    Swati Andhale,

  • Keshav Pradip Bave,

  • Pavan Gulabrao Wakale,

  1. Assistant Professor, Department of Master of Computer Application, Genba Sopanrao Moze Trust Parvatibai Genba Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  2. Student, Department of Master of Computer Application, Genba Sopanrao Moze Trust Parvatibai Genba Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  3. Student, Department of Master of Computer Application, Genba Sopanrao Moze Trust Parvatibai Genba Moze College of Engineering, Wagholi, Pune, Maharashtra, India

Abstract

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 labelled X-ray images, pre-processed 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 study 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

[This article belongs to International Journal of Optical Innovations & Research ]

How to cite this article:
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):17-22.
How to cite this URL:
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):17-22. Available from: https://journals.stmjournals.com/ijoir/article=2024/view=185592


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Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 09/10/2024
Accepted 13/11/2024
Published 25/11/2024
392

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