Convolutional Neural Network Model for Bone Fracture Detection and Classification in X-Ray Images
Bone fractures are one of the most common medical conditions worldwide. Proper and rapid diagnosis of fractures is essential to ensure effective treatment and reduce the risk of further complications. This study uses a Convolutional Neural Network (CNN) for fracture classification on X-ray images...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2025/ http://eprints.intimal.edu.my/2025/1/jods2024_43.pdf |
| _version_ | 1848766900222296064 |
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| author | M. Fariz Fadillah, Mardianto Elly, Pusporani Fatiha Nadia, Salsabila Alfi Nur, Nitasari |
| author_facet | M. Fariz Fadillah, Mardianto Elly, Pusporani Fatiha Nadia, Salsabila Alfi Nur, Nitasari |
| author_sort | M. Fariz Fadillah, Mardianto |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Bone fractures are one of the most common medical conditions worldwide. Proper and rapid
diagnosis of fractures is essential to ensure effective treatment and reduce the risk of further
complications. This study uses a Convolutional Neural Network (CNN) for fracture classification
on X-ray images, which aims for the clinical implementation of CNN models in supporting the
diagnostic process in the orthopedic field to minimize misdiagnosis due to human error. The
analysis results show that fracture classification using CNN has accuracy, precision, recall, and
F1-score reaching 99%, indicating highly accurate classification performance. This research aligns
with the 3rd SDG's goal of good health and well-being: to ensure a healthy life and support wellbeing.
The results of this research are expected to significantly contribute to the medical world,
especially in improving the accuracy and efficiency of fracture diagnosis and become a foundation
for developing more innovative diagnostic technologies to support more equitable and quality
health services globally. |
| first_indexed | 2025-11-14T11:58:29Z |
| format | Article |
| id | intimal-2025 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:29Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20252024-12-31T07:27:11Z http://eprints.intimal.edu.my/2025/ Convolutional Neural Network Model for Bone Fracture Detection and Classification in X-Ray Images M. Fariz Fadillah, Mardianto Elly, Pusporani Fatiha Nadia, Salsabila Alfi Nur, Nitasari QA75 Electronic computers. Computer science QA76 Computer software RC Internal medicine Bone fractures are one of the most common medical conditions worldwide. Proper and rapid diagnosis of fractures is essential to ensure effective treatment and reduce the risk of further complications. This study uses a Convolutional Neural Network (CNN) for fracture classification on X-ray images, which aims for the clinical implementation of CNN models in supporting the diagnostic process in the orthopedic field to minimize misdiagnosis due to human error. The analysis results show that fracture classification using CNN has accuracy, precision, recall, and F1-score reaching 99%, indicating highly accurate classification performance. This research aligns with the 3rd SDG's goal of good health and well-being: to ensure a healthy life and support wellbeing. The results of this research are expected to significantly contribute to the medical world, especially in improving the accuracy and efficiency of fracture diagnosis and become a foundation for developing more innovative diagnostic technologies to support more equitable and quality health services globally. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2025/1/jods2024_43.pdf M. Fariz Fadillah, Mardianto and Elly, Pusporani and Fatiha Nadia, Salsabila and Alfi Nur, Nitasari (2024) Convolutional Neural Network Model for Bone Fracture Detection and Classification in X-Ray Images. Journal of Data Science, 2024 (43). pp. 1-6. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software RC Internal medicine M. Fariz Fadillah, Mardianto Elly, Pusporani Fatiha Nadia, Salsabila Alfi Nur, Nitasari Convolutional Neural Network Model for Bone Fracture Detection and Classification in X-Ray Images |
| title | Convolutional Neural Network Model for Bone Fracture Detection and
Classification in X-Ray Images |
| title_full | Convolutional Neural Network Model for Bone Fracture Detection and
Classification in X-Ray Images |
| title_fullStr | Convolutional Neural Network Model for Bone Fracture Detection and
Classification in X-Ray Images |
| title_full_unstemmed | Convolutional Neural Network Model for Bone Fracture Detection and
Classification in X-Ray Images |
| title_short | Convolutional Neural Network Model for Bone Fracture Detection and
Classification in X-Ray Images |
| title_sort | convolutional neural network model for bone fracture detection and
classification in x-ray images |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RC Internal medicine |
| url | http://eprints.intimal.edu.my/2025/ http://eprints.intimal.edu.my/2025/ http://eprints.intimal.edu.my/2025/1/jods2024_43.pdf |