Osteoporosis is a systemic bone disease that characterized by an increase in bone fragility due to bone microstructure damage. Currently, osteoporosis is diagnosed clinically and confirmed by Dual-energy X-ray absorptiometry (DXA), which mainly depends on bone density and somehow being subjective. This study aimed to develop a deep learning method combined with bone tissue microstructure for the early diagnosis of osteoporosis. First, we applied Gabor filters to preprocess the raw osteoporotic MRI images in three scales and three directions for data augmentation. Second, we proposed a novel hybrid CNN-HKNN system which combines convolutional neural network (CNN) with k-local hyperplane distance nearest neighbour algorithm (HKNN) for osteoporotic MRI classification. Third, we introduced a transfer learning technique by pre-training the CNN model with the augmented dataset to improve the robustness of the proposed model. Experiments under 10-fold cross-validation showed accuracy of the system is 0.963, and the area under the receiver operating characteristic curve (AUC) was 0.980. In conclusion, the proposed method has an excellent ability to diagnose osteoporosis, which has certain clinical application prospects.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.