relation: http://repositorio.unic.co.ao/id/eprint/16270/ canonical: http://repositorio.unic.co.ao/id/eprint/16270/ title: Novel transfer learning based bone fracture detection using radiographic images creator: Alam, Aneeza creator: Al-Shamayleh, Ahmad Sami creator: Thalji, Nisrean creator: Raza, Ali creator: Morales Barajas, Edgar Aníbal creator: Bautista Thompson, Ernesto creator: de la Torre Diez, Isabel creator: Ashraf, Imran subject: Ingeniería description: 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. date: 2025-01 type: Artículo type: PeerReviewed format: text language: en rights: cc_by_nc_nd_4 identifier: http://repositorio.unic.co.ao/id/eprint/16270/1/s12880-024-01546-4.pdf identifier: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Artículos y libros Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Alam, Aneeza; Al-Shamayleh, Ahmad Sami; Thalji, Nisrean; Raza, Ali; Morales Barajas, Edgar Aníbal; Bautista Thompson, Ernesto; de la Torre Diez, Isabel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Novel transfer learning based bone fracture detection using radiographic images. BMC Medical Imaging, 25 (1). ISSN 1471-2342 relation: http://doi.org/10.1186/s12880-024-01546-4 relation: doi:10.1186/s12880-024-01546-4 language: en