Abstract:
The fractures of the femur and pelvis
are life-threatening orthopedic conditions that are
common among older individuals, causing severe
complications and reducing mobility. This paper
introduces a deep learning method for automati
cally recognizing pelvic and femur fractures in X-ray
images. Our approach relies on ensemble deep learn
ing models such as convolutional neural network
(CNN) architectures, including ResNet50, Incep
tionV3, ResNet101, EfficientNetB0, EfficientNetV2,
MobileNet and Xception, which classify fractures
into five possible categories: non-displaced, incom
plete non-displaced, complete non-displaced, par
tially displaced, and fully displaced fractures. Data,
comprising around 1000 X-ray images from vari
ous hospitals, were pre-processed and augmented to
strengthen the model. The ResNet50 model achieved
the highest classification accuracy at 80% on the test
set and was identified as the best-performing model
for distinguishing fracture types. The framework
combines modern feature engineering and ensemble
learning models to enable early and accurate diag
nosis, leading to improved clinical outcomes in treat
ing femur and pelvic fractures, reducing diagnostic
errors, and significantly enhancing the diagnostic
process.
