Abstract:
Osteoporosis is a progressive bone disease that leads to a higher risk of fractures,
and early diagnosis continues to be a significant problem because current diagnostic
tools are not comprehensive. In this work, we tested deep learning models, such as
VGG16, VGG19, ResNet, DenseNet, UNet, Vision Transformer (ViT), and Swin
Transformer, on 853 X-ray images to detect osteoporosis automatically. To
overcome the issue of class imbalance and enhance the robustness of the model,
data preprocessing was carried out through standardization, normalization and
augmentation. It was experimentally demonstrated that VGG19 had the best
classification accuracy of 97%, and UNet performed better in a segmentation task
with a Dice score of 0.936. In comparison with the available literature, our method
shows a competitive level of performance and proves the possibility of CNN-based
and transformer models in medical image processing. The present work helps to
build trustworthy, inexpensive, and affordable AI-aided diagnostic systems to
monitor osteoporosis and establishes the future trends towards larger datasets,
multimodal systems, and real-time clinical use.
