4 Conclusion
In this paper, a new classification algorithm based on deep convolutional neural network is proposed for radioactive musculoskeletal images. Adaptive histogram equalization with limited contrast was used to improve the training image quality, and the
image texture features were merged and sent to DenseNet169 for training and classification. Experimental analysis shows that the proposed algorithm can effectively
improve the classification accuracy of radioactive musculoskeletal images.
Further work in this paper is to continue to optimize the algorithm and expand the
dataset to train a new depth neural network sensitive to radioactive musculoskeletal
images.
Acknowledgements. This study was supported by the National Natural Science Foundation of
China (NSFC) under Grant No. 61563039.
References
1. Qin D, Wang S, Zhang Z, He L (2017) Advances in research on discrimination criteria for
work-related musculoskeletal disorders. Chin Occupational Med (3)
2. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image
classification using convolutional neural networks. In: International joint conference on neural
networks, IEEE
3. Wei L, Peng C, Dazhe Z, Junbo W (2016) Pulmonary nodule classification with deep
convolutional neural networks on computed tomography images. Comput Math Methods Med
2016:1–7
4. Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S et al (2014) A new 2.5 D
representation for lymph node detection using random sets of deep convolutional neural
network observations. Med Image Comput Comput Assist Interv
5. Pizer S (1987) Adaptive histogram equalization and its variations. Comput Vis Image Process
39(3):355–368
6. Huang G, Liu Z, Laurens VDM, Weinberger KQ (2016) Densely connected convolutional
networks
7. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant
texture classification with local binary patterns. IEEE Trans Pattern Anal & Mach Intell
8. Rajpurkar P, Irvin J, Bagul A, Ding D, Duan T, Mehta H et al (2017) Mura: large dataset for
abnormality detection in musculoskeletal radiographs
486
Y. Shao and X. Wang
In this paper, a new classification algorithm based on deep convolutional neural network is proposed for radioactive musculoskeletal images. Adaptive histogram equalization with limited contrast was used to improve the training image quality, and the
image texture features were merged and sent to DenseNet169 for training and classification. Experimental analysis shows that the proposed algorithm can effectively
improve the classification accuracy of radioactive musculoskeletal images.
Further work in this paper is to continue to optimize the algorithm and expand the
dataset to train a new depth neural network sensitive to radioactive musculoskeletal
images.
Acknowledgements. This study was supported by the National Natural Science Foundation of
China (NSFC) under Grant No. 61563039.
References
1. Qin D, Wang S, Zhang Z, He L (2017) Advances in research on discrimination criteria for
work-related musculoskeletal disorders. Chin Occupational Med (3)
2. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image
classification using convolutional neural networks. In: International joint conference on neural
networks, IEEE
3. Wei L, Peng C, Dazhe Z, Junbo W (2016) Pulmonary nodule classification with deep
convolutional neural networks on computed tomography images. Comput Math Methods Med
2016:1–7
4. Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S et al (2014) A new 2.5 D
representation for lymph node detection using random sets of deep convolutional neural
network observations. Med Image Comput Comput Assist Interv
5. Pizer S (1987) Adaptive histogram equalization and its variations. Comput Vis Image Process
39(3):355–368
6. Huang G, Liu Z, Laurens VDM, Weinberger KQ (2016) Densely connected convolutional
networks
7. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant
texture classification with local binary patterns. IEEE Trans Pattern Anal & Mach Intell
8. Rajpurkar P, Irvin J, Bagul A, Ding D, Duan T, Mehta H et al (2017) Mura: large dataset for
abnormality detection in musculoskeletal radiographs
486
Y. Shao and X. Wang