eprintid: 16270 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/62/70 datestamp: 2025-01-22 23:30:08 lastmod: 2025-01-22 23:30:09 status_changed: 2025-01-22 23:30:08 type: article metadata_visibility: show creators_name: Alam, Aneeza creators_name: Al-Shamayleh, Ahmad Sami creators_name: Thalji, Nisrean creators_name: Raza, Ali creators_name: Morales Barajas, Edgar Aníbal creators_name: Bautista Thompson, Ernesto creators_name: de la Torre Diez, Isabel creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: ernesto.bautista@unini.edu.mx creators_id: creators_id: title: Novel transfer learning based bone fracture detection using radiographic images ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Radiographic images; Bone fractures; Deep learning; Transfer learning; Image processing 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. date: 2025-01 publication: BMC Medical Imaging volume: 25 number: 1 id_number: doi:10.1186/s12880-024-01546-4 refereed: TRUE issn: 1471-2342 official_url: http://doi.org/10.1186/s12880-024-01546-4 access: open language: en citation: 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 document_url: http://repositorio.unic.co.ao/id/eprint/16270/1/s12880-024-01546-4.pdf