Staff View: Convolutional Neural Network Model for Bone Fracture Detection and Classification in X-Ray Images

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...

Full description

Saved in:

Bibliographic Details
Main Authors:	M. Fariz Fadillah, Mardianto, Elly, Pusporani, Fatiha Nadia, Salsabila, Alfi Nur, Nitasari
Format:	Article
Language:	English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software RC Internal medicine
Online Access:	http://eprints.intimal.edu.my/2025/1/jods2024_43.pdf http://eprints.intimal.edu.my/2025/ http://ipublishing.intimal.edu.my/jods.html
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-inti-eprints.2025
record_format	eprints
spelling	my-inti-eprints.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
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software RC Internal medicine
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
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.
format	Article
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
title	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_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_sort	convolutional neural network model for bone fracture detection and classification in x-ray images
publisher	INTI International University
publishDate	2024
url	http://eprints.intimal.edu.my/2025/1/jods2024_43.pdf http://eprints.intimal.edu.my/2025/ http://ipublishing.intimal.edu.my/jods.html
_version_	1821008753827250176
score	13.235362

Convolutional Neural Network Model for Bone Fracture Detection and Classification in X-Ray Images

Similar Items