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ORIGINAL RESEARCH article

Front. Med.
Sec. Pathology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1506686
This article is part of the Research Topic Artificial Intelligence-Assisted Medical Imaging Solutions for Integrating Pathology and Radiology Automated Systems - Volume II View all 7 articles

Diagnosis and Detection of Bone Fracture in Radiographic Images Using Deep Learning Approaches

Provisionally accepted
Theyazn Aldhyani Theyazn Aldhyani 1Zeyad A.T. Ahmed Zeyad A.T. Ahmed 2Bayan M. Alsharbi Bayan M. Alsharbi 3Sultan Ahmad Sultan Ahmad 4*Mosleh Hmoud Al-Adhaileh Mosleh Hmoud Al-Adhaileh 5Ahmed Hassan Kamaland Ahmed Hassan Kamaland 6Mohammed Almaiah Mohammed Almaiah 7Jabeen Nazeer Jabeen Nazeer 4
  • 1 College of Applied Sciences, King Faisal University, AlAhsa, Eastern Province, Saudi Arabia
  • 2 Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Madhya Pradesh, India
  • 3 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia, Riyadh, Riyadh, Saudi Arabia
  • 4 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
  • 5 Deanship of E-Learning and Distance Education and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
  • 6 College of medicine, Department of orthopedic and trauma, King Faisal University, Al-Ahsa, Saudi Arabia, Al-Ahsa, Saudi Arabia
  • 7 King Abdullah II School of Information Technology, The University of Jordan, Aljubeiha, Amman, Jordan

The final, formatted version of the article will be published soon.

    Bones are a fundamental component of human anatomy, enabling movement and support. Bone fractures are prevalent in the human body, and their accurate diagnosis is crucial in medical practice. In response to this challenge, researchers have turned to deeplearning (DL) algorithms. Recent advancements in sophisticated DL methodologies have helped overcome existing issues in fracture detection. Nevertheless, it is essential to develop an automated approach for identifying fractures using the multi-region X-ray dataset from Kaggle, which contains a comprehensive collection of 10,580 radiographic images. This study advocates for the use of DL techniques, including VGG16, ResNet152V2, and DenseNet201, for the detection and diagnosis of bone fractures. The experimental findings demonstrate that the proposed approach accurately identifies and classifies various types of fractures. Our system, incorporating DenseNet201 and VGG16, achieved an accuracy rate of 97% during the validation phase. By addressing these challenges, we can further improve DL models for fracture detection. This article tackles the limitations of existing methods for fracture .detection and diagnosis and proposes a system that improves accuracy. The findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis.

    Keywords: deep learning, artificial intelligence, Radiographic images, Bone fractures, diagnosis

    Received: 05 Oct 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 Aldhyani, Ahmed, Alsharbi, Ahmad, Al-Adhaileh, Kamaland, Almaiah and Nazeer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sultan Ahmad, Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.