CNN-HKNN for osteoporosis magnetic resonance imaging classification with data augmentation

Shuting Lin; Lin Zou; Yan Li; Zhuozhi Dai; Teng Zhou; Gang Xiao; Gang Guo; Renhua Wu; Guishan Zhang; Yaowen Chen

doi:10.1117/12.2600463

1 June 2021 CNN-HKNN for osteoporosis magnetic resonance imaging classification with data augmentation

Shuting Lin, Lin Zou, Yan Li, Zhuozhi Dai, Teng Zhou, Gang Xiao, Gang Guo, Renhua Wu, Guishan Zhang, Yaowen Chen

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 118481E (2021) https://doi.org/10.1117/12.2600463
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

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.

Citation Download Citation

Shuting Lin, Lin Zou, Yan Li, Zhuozhi Dai, Teng Zhou, Gang Xiao, Gang Guo, Renhua Wu, Guishan Zhang, and Yaowen Chen "CNN-HKNN for osteoporosis magnetic resonance imaging classification with data augmentation", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 118481E (1 June 2021); https://doi.org/10.1117/12.2600463

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