Novel transfer learning based bone fracture detection using radiographic images
Abstract A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one yea...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-024-01546-4 |
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| author | Aneeza Alam Ahmad Sami Al-Shamayleh Nisrean Thalji Ali Raza Edgar Anibal Morales Barajas Ernesto Bautista Thompson Isabel de la Torre Diez Imran Ashraf |
| author_facet | Aneeza Alam Ahmad Sami Al-Shamayleh Nisrean Thalji Ali Raza Edgar Anibal Morales Barajas Ernesto Bautista Thompson Isabel de la Torre Diez Imran Ashraf |
| author_sort | Aneeza Alam |
| collection | DOAJ |
| description | Abstract A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients. The detection of bone fractures is crucial, and radiographic images are often relied on for accurate assessment. An efficient neural network method is essential for the early detection and timely treatment of fractures. In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. Several machine learning (ML) techniques are applied to the subsets of newly generated transfer features to compare the results. K-nearest neighbor (KNN), LGBM, logistic regression (LR), and random forest (RF) are implemented using the novel features with optimized hyperparameters. The LGBM and LR models trained on proposed MobLG-Net (MobileNet-LGBM) based features outperformed others, achieving an accuracy of 99% in predicting bone fractures. A cross-validation mechanism is used to evaluate the performance of each model. The proposed study can improve the detection of bone fractures using X-ray images. |
| format | Article |
| id | doaj-art-018693667e2c4f4496634e45fde757fa |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-018693667e2c4f4496634e45fde757fa2025-01-05T12:50:08ZengBMCBMC Medical Imaging1471-23422025-01-0125111610.1186/s12880-024-01546-4Novel transfer learning based bone fracture detection using radiographic imagesAneeza Alam0Ahmad Sami Al-Shamayleh1Nisrean Thalji2Ali Raza3Edgar Anibal Morales Barajas4Ernesto Bautista Thompson5Isabel de la Torre Diez6Imran Ashraf7Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering & Information TechnologyDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman UniversityFaculty of Computer Studies, Arab Open UniversityDepartment of Software Engineering, University of LahoreUniversidad Europea del AtlanticoUniversidad Europea del AtlanticoDepartment of Signal Theory, Communications and Telematics Engineering, Unviersity of ValladolidDepartment of Information and Communication Engineering, Yeungnam UniversityAbstract A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients. The detection of bone fractures is crucial, and radiographic images are often relied on for accurate assessment. An efficient neural network method is essential for the early detection and timely treatment of fractures. In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. Several machine learning (ML) techniques are applied to the subsets of newly generated transfer features to compare the results. K-nearest neighbor (KNN), LGBM, logistic regression (LR), and random forest (RF) are implemented using the novel features with optimized hyperparameters. The LGBM and LR models trained on proposed MobLG-Net (MobileNet-LGBM) based features outperformed others, achieving an accuracy of 99% in predicting bone fractures. A cross-validation mechanism is used to evaluate the performance of each model. The proposed study can improve the detection of bone fractures using X-ray images.https://doi.org/10.1186/s12880-024-01546-4Radiographic imagesBone fracturesDeep learningTransfer learningImage processing |
| spellingShingle | Aneeza Alam Ahmad Sami Al-Shamayleh Nisrean Thalji Ali Raza Edgar Anibal Morales Barajas Ernesto Bautista Thompson Isabel de la Torre Diez Imran Ashraf Novel transfer learning based bone fracture detection using radiographic images BMC Medical Imaging Radiographic images Bone fractures Deep learning Transfer learning Image processing |
| title | Novel transfer learning based bone fracture detection using radiographic images |
| title_full | Novel transfer learning based bone fracture detection using radiographic images |
| title_fullStr | Novel transfer learning based bone fracture detection using radiographic images |
| title_full_unstemmed | Novel transfer learning based bone fracture detection using radiographic images |
| title_short | Novel transfer learning based bone fracture detection using radiographic images |
| title_sort | novel transfer learning based bone fracture detection using radiographic images |
| topic | Radiographic images Bone fractures Deep learning Transfer learning Image processing |
| url | https://doi.org/10.1186/s12880-024-01546-4 |
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