Combined algorithm for bone age determination based on hand x-rays analysis
Journal of the Belarusian State University. Mathematics and Informatics, Tome 2 (2020), pp. 105-114.

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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
Keywords: bone age; X-ray; radiograph; image processing; activation map; convolutional neural network; automation of diagnostics.
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I. I. Kosik; A. M. Nedzvedz; R. M. Karapetsian. Combined algorithm for bone age determination based on hand x-rays analysis. Journal of the Belarusian State University. Mathematics and Informatics, Tome 2 (2020), pp. 105-114. http://geodesic.mathdoc.fr/item/BGUMI_2020_2_a10/

[1] E. Baykal, H. Dogan, M. E. Ercin, S. Ersoz, M. Ekinci, “Transfer learning with pre-trained deep convolutional neural networks for serous cell classification”, Multimedia Tools and Applications, 79(21–22) (2020), 15593–15611 | DOI

[2] J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, L. Fei-Fei, “ImageNet: a large-scale hierarchical image database”, IEEE conference on computer vision and pattern recognition (Miami, Florida, USA). Institute of Electrical and Electronics Engineers, 2009, 248–255 | DOI

[3] A. Dzhulli, S. Pal, “Biblioteka Keras – instrument glubokogo obucheniya: realizatsiya neironnykh setei s pomoschyu bibliotek Theano i TensorFlow”, Moskva: DMK Press, 2017, 294

[4] F. Chollet, “Xception: deep learning with depthwise separable convolutions”, IEEE conference on computer vision and pattern recognition (CVPR) (Honolulu, Hawaii, USA). Institute of Electrical and Electronics Engineers, 2017, 1800–1807 | DOI

[5] G. Huang, Z. Liu, Der. Van, K. Q. Weinberger, “Densely connected convolutional networks”, IEEE conference on computer vision and pattern recognition (CVPR) (Honolulu, Hawaii, USA). Institute of Electrical and Electronics Engineers, 2017, 2261–2269 | DOI

[6] A. Khan, A. Sohail, U. Zahoora, A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks”, Artificial Intelligence Review, 2020, 1–70 | DOI | Zbl

[7] T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, “Feature pyramid networks for object detection”, IEEE conference on computer vision and pattern recognition (CVPR) (Honolulu, Hawaii, USA). Institute of Electrical and Electronics Engineers, 2017, 936–944 | DOI

[8] “Machine learning: concepts, methodologies, tools and applications”, Hershey: Information Science Reference, 2011, 2141

[9] M. I. Jordan, R. A. Jacobs, “Hierarchical mixtures of experts and the EM algorithm”, Neural Computation, 6(2) (1994), 181–214 | DOI

[10] D. H. Wolpert, “Stacked generalization”, Neural Networks, 5(2) (1992), 241–259 | DOI | MR

[11] E. Menahem, L. Rokach, Y. Elovici, “Troika – an improved stacking schema for classification tasks”, Information Sciences, 179(24) (2009), 4097–4122 | DOI

[12] A. K. Seewald, “How to make stacking better and faster while also taking care of an unknown weakness”, Proceedings of the 19?th International conference on machine learning, 2002, 554–561, San Francisco: Morgan Kaufmann Publishers