Development of Technology for Analysing X-Ray Images of Bone Tissue for Computer Diagnosis of Osteoporosis

Daria V. Nekrasova; Nataly Yu. Ilyasova; Nikita S. Demin; Sergey S. Pervushkin

doi:10.18287/JBPE26.12.020303

Authors

Daria V. Nekrasova Samara National Research University, Russian Federation
Nataly Yu. Ilyasova Samara National Research University, Russian Federation IPSI, NRC “Kurchatov Institute”, Samara, Russian Federation
Nikita S. Demin Samara National Research University, Russian Federation IPSI, NRC “Kurchatov Institute”, Samara, Russian Federation
Sergey S. Pervushkin FSBEI HE “SamSMU” of the Ministry of Health of the Russian Federation, Samara, Russian Federation

DOI:

https://doi.org/10.18287/JBPE26.12.020303

Keywords:

osteoporosis, radiography, machine learning, resnet, deeplab

Abstract

The interpretation of lumbar spine radiographs, the most accessible diagnostic modality for osteoporosis, is often subjective and hampered by limited accuracy. To address this, we developed a neural network-based algorithm for computer-aided diagnosis. This study provides a comparative analysis of key neural network architectures for image segmentation and classification. The proposed system, with a classification accuracy of 87.42%, offers a valuable adjunct for radiologists, mitigating diagnostic risks and enhancing the overall efficacy of osteoporosis screening

References

J. A. Kanis, C. Cooper, R. Rizzoli, and J. Y. Reginster, “European guidance for the diagnosis and management of osteoporosis in postmenopausal women,” Osteoporosis International 30(1), 3–44 (2019).

G. Guglielmi (Ed.), Osteoporosis and Bone Densitometry Measurements, Springer Berlin, Heidelberg (2013). ISBN: 978-3-642-27884-6 (eBook).

E. M. Eidlyna, G. V. Diachkova, and K. A. Diachkov, “Modern radiation diagnosing the spine pathological fractures thro ugh osteoporosis,” Genij Ortopedii 2, 38–43 (2012). [in Russian]

A. V. Petraikin, Zh. E. Belaya, A. N. Kiseleva, Z. R. Artyukova, M. G. Belyaev, V. A. Kondratenko, M. E. Pisov, A. V. Solovev, A. K. Smorchkova, L. R. Abuladze, I. N. Kieva, V. A. Fedanov, L. R. Iassin, D. S. Semenov, N. D. Kudryavtsev, S. P. Shchelykalina, V. V. Zinchenko, E. S. Akhmad, K. A. Sergunova, V. A. Gombolevsky, L. A. Nisovstova, A. V. Vladzymyrskyy, and S. P. Morozov, “Artificial intelligence technology for recognition of vertebral compression fractures using a morphometric analysis model based on convolutional neural networks,” Problems of Endocrinology 66(5), 48–60 (2020). [in Russian]

Zh. A. Klimova, A. A. Zaft, and V. B. Zaft, “Modern laboratory diagnosis of osteoporosis,” International Journal of Endocrinology 7.63, 75–84 (2014).

A. Gaidel, S. Pervushkin, “Research of the textural features for the bony tissue diseases diagnostics using the roentgenograms,” Computer Optics 37(1), 113–119 (2013).

I. A. Belozerov, V.A. Sudakov, “Investigation of machine learning models for medical image segmentation,” Keldysh Institute Preprints 37, 1–15 (2022).

Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: a nested U-Net architecture for medical image segmentation,” Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 11045, 3–11 (2018).

I. A. Lozhkin, M. E. Dunaev, K. S. Zaytsev, and A. A. Garmash, “Augmentation of image datasets for training neural networks in semantic segmentation tasks,” International Journal of Open Information Technologies 11(1), 109–117 (2023).

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Medical Image Computing and Computer-Assisted Intervention 9351, 234–241 (2015).

L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” European Conference on Computer Vision 11211, 801–818 (2018).

G. Amiya, K. Ramaraj, P.R. Murugan, V. Govindaraj, M. Vasudevan, and A. Thiyagarajan, “A review on automated algorithms used for osteoporosis diagnosis,” Lecture Notes in Networks and Systems. Inventive Systems and Control 436, 247–262 (2022).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (2016).

D. Sarwinda, R. H. Paradisa, A. Bustamam, and P. Anggia, “Deep learning in image classification using residual network (resnet) variants for detection of colorectal cancer,” Procedia Computer Science 179, 423–431 (2021).

M. Tan, Q. V. Le, “EfficientNet: rethinking model scaling for convolutional neural networks,” International Conference on Machine Learning 97, 6105–6114 (2019).