TY  - JOUR
KW  - Radiographic images; Bone fractures; Deep learning; Transfer learning; Image processing
AV  - public
IS  - 1
TI  - Novel transfer learning based bone fracture detection using radiographic images
Y1  - 2025/01//
UR  - http://doi.org/10.1186/s12880-024-01546-4
JF  - BMC Medical Imaging
A1  - Alam, Aneeza
A1  - Al-Shamayleh, Ahmad Sami
A1  - Thalji, Nisrean
A1  - Raza, Ali
A1  - Morales Barajas, Edgar Aníbal
A1  - Bautista Thompson, Ernesto
A1  - de la Torre Diez, Isabel
A1  - Ashraf, Imran
SN  - 1471-2342
VL  - 25
ID  - unic16270
N2  - 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.
ER  -