Diagnosis and detection of bone fracture in radiographic images using deep learning approaches
IntroductionBones 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 deep-learning (DL) algorithms. Recent...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1506686/full |
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| author | Theyazn Aldhyani Zeyad A. T. Ahmed Bayan M. Alsharbi Sultan Ahmad Sultan Ahmad Mosleh Hmoud Al-Adhaileh Ahmed Hassan Kamal Mohammed Almaiah Jabeen Nazeer |
| author_facet | Theyazn Aldhyani Zeyad A. T. Ahmed Bayan M. Alsharbi Sultan Ahmad Sultan Ahmad Mosleh Hmoud Al-Adhaileh Ahmed Hassan Kamal Mohammed Almaiah Jabeen Nazeer |
| author_sort | Theyazn Aldhyani |
| collection | DOAJ |
| description | IntroductionBones 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 deep-learning (DL) algorithms. Recent advancements in sophisticated DL methodologies have helped overcome existing issues in fracture detection.MethodsNevertheless, 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.ResultsThe 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.ConclusionThe findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis. |
| format | Article |
| id | doaj-art-fc7e9ac75b1c40c796db658fd2399344 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-fc7e9ac75b1c40c796db658fd23993442025-01-24T12:15:31ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011110.3389/fmed.2024.15066861506686Diagnosis and detection of bone fracture in radiographic images using deep learning approachesTheyazn Aldhyani0Zeyad A. T. Ahmed1Bayan M. Alsharbi2Sultan Ahmad3Sultan Ahmad4Mosleh Hmoud Al-Adhaileh5Ahmed Hassan Kamal6Mohammed Almaiah7Jabeen Nazeer8Applied College, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, IndiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi ArabiaSchool of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, IndiaDeanship of E-Learning and Distance Education and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Orthopedic and Trauma, College of Medicine, King Faisal University, Al-Ahsa, Saudi ArabiaKing Abdullah the II IT School, The University of Jordan, Amman, JordanDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi ArabiaIntroductionBones 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 deep-learning (DL) algorithms. Recent advancements in sophisticated DL methodologies have helped overcome existing issues in fracture detection.MethodsNevertheless, 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.ResultsThe 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.ConclusionThe findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis.https://www.frontiersin.org/articles/10.3389/fmed.2024.1506686/fulldeep learningartificial intelligenceradiographic imagesbone fracturesdiagnosis |
| spellingShingle | Theyazn Aldhyani Zeyad A. T. Ahmed Bayan M. Alsharbi Sultan Ahmad Sultan Ahmad Mosleh Hmoud Al-Adhaileh Ahmed Hassan Kamal Mohammed Almaiah Jabeen Nazeer Diagnosis and detection of bone fracture in radiographic images using deep learning approaches Frontiers in Medicine deep learning artificial intelligence radiographic images bone fractures diagnosis |
| title | Diagnosis and detection of bone fracture in radiographic images using deep learning approaches |
| title_full | Diagnosis and detection of bone fracture in radiographic images using deep learning approaches |
| title_fullStr | Diagnosis and detection of bone fracture in radiographic images using deep learning approaches |
| title_full_unstemmed | Diagnosis and detection of bone fracture in radiographic images using deep learning approaches |
| title_short | Diagnosis and detection of bone fracture in radiographic images using deep learning approaches |
| title_sort | diagnosis and detection of bone fracture in radiographic images using deep learning approaches |
| topic | deep learning artificial intelligence radiographic images bone fractures diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1506686/full |
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