Combined algorithm for bone age determination based on hand X-rays analysis
Abstract
In this paper, we investigate the urgent problem associated with bone age determination using hand X-rays. A combined algorithm for the recognition of radiographs is proposed, which uses simultaneous two neural network models, based on Xception and DenseNet169. The method allows to generalize the knowledge of different medical experts and increases the accuracy of bone age prediction in general.
References
- Baykal E, Dogan H, Ercin ME, Ersoz S, Ekinci M. Transfer learning with pre-trained deep convolutional neural networks for serous cell classification. Multimedia Tools and Applications. 2020;79(21–22):15593–15611. DOI: 10.1007/s11042-019-07821-9.
- Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition; 2009 June 20–25; Miami, Florida, USA. [S. l.]: Institute of Electrical and Electronics Engineers; 2009. p. 248–255. DOI: 10.1109/CVPR.2009.5206848.
- Gulli A, Pal S. Deep learning with Keras: implement neural networks with Keras on Theano and TensorFlow. Birmingham: Packt Publishing; 2017. 320 p. Russian edition: Gulli A, Pal S. Biblioteka Keras – instrument glubokogo obucheniya: realizatsiya neironnykh setei s pomoshch’ yu bibliotek Theano i TensorFlow. Slinkin AA, translator. Moscow: DMK Press; 2017. 294 p.
- Chollet F. Xception: deep learning with depthwise separable convolutions. In: IEEE conference on computer vision and pattern recognition (CVPR); 2017 July 21–26; Honolulu, Hawaii, USA. [S. l.]: Institute of Electrical and Electronics Engineers; 2017. p. 1800–1807. DOI: 10.1109/CVPR.2017.195.
- Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition (CVPR); 2017 July 21–26; Honolulu, Hawaii, USA. [S. l.]: Institute of Electrical and Electronics Engineers; 2017. p. 2261–2269. DOI: 10.1109/CVPR.2017.243.
- Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review. 2020:1–70. DOI: 10.1007/s10462-020-09825-6.
- Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition (CVPR); 2017 July 21–26; Honolulu, Hawaii, USA. [S. l.]: Institute of Electrical and Electronics Engineers; 2017. p. 936–944. DOI: 10.1109/CVPR.2017.106.
- Machine learning: concepts, methodologies, tools and applications. Hershey: Information Science Reference; 2011. 2141 p. (Premier reference source).
- Jordan MI, Jacobs RA. Hierarchical mixtures of experts and the EM algorithm. Neural Computation. 1994;6(2):181–214. DOI: 10.1162/neco.1994.6.2.181.
- Wolpert DH. Stacked generalization. Neural Networks. 1992;5(2):241–259. DOI: 10.1016/S0893-6080(05)80023-1.
- Menahem E, Rokach L, Elovici Y. Troika – an improved stacking schema for classification tasks. Information Sciences. 2009; 179(24):4097–4122. DOI: 10.1016/j.ins.2009.08.025.
- Seewald AK. How to make stacking better and faster while also taking care of an unknown weakness. In: Sammut C, Hoffmann AG, editors. ICML’02. Proceedings of the 19 th International conference on machine learning. San Francisco: Morgan Kaufmann Publishers; 2002. p. 554–561.
Copyright (c) 2020 Journal of the Belarusian State University. Mathematics and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who are published in this journal agree to the following:
- The authors retain copyright on the work and provide the journal with the right of first publication of the work on condition of license Creative Commons Attribution-NonCommercial. 4.0 International (CC BY-NC 4.0).
- The authors retain the right to enter into certain contractual agreements relating to the non-exclusive distribution of the published version of the work (e.g. post it on the institutional repository, publication in the book), with the reference to its original publication in this journal.
- The authors have the right to post their work on the Internet (e.g. on the institutional store or personal website) prior to and during the review process, conducted by the journal, as this may lead to a productive discussion and a large number of references to this work. (See The Effect of Open Access.)