Using an Ensemble of Neural Networks for Determining the Diagnostic Parameters of the Vertebrae

Journal Journal of Mechanical Engineering – Problemy Mashynobuduvannia
Publisher Anatolii Pidhornyi Institute for Mechanical Engineering Problems
of National Academy of Science of Ukraine
ISSN  2709-2984 (Print), 2709-2992 (Online)
Issue Vol. 27, no. 1, 2024 (March)
Pages 56-61
Cited by J. of Mech. Eng., 2024, vol. 27, no. 1, pp. 56-61



Vladyslav D. Koniukhov, Anatolii Pidhornyi Institute of Mechanical Engineering Problems of NAS of Ukraine (2/10, Pozharskyi str., Kharkiv, 61046, Ukraine), e-mail:, ORCID: 0009-0007-0256-1388



Artificial intelligence opens up great prospects in many areas of human activity, primarily in medicine. One of the priority directions of using artificial intelligence in this field is the segmentation of medical images for the purpose of automatic diagnosis of common diseases. The application of neural network approaches to image analysis of medical images is becoming an increasingly promising direction in the field of medical diagnostics. In particular, this paper investigates the possibility of using an ensemble of neural networks for diagnosing osteoporosis. To achieve this goal, a study was conducted on the possibility of using machine learning methods to segment and determine the shape and size of certain vertebrae: Th8, Th9, Th10, Th11 of a human vertebra on X-ray images obtained in real conditions. Each network is configured and tested on different sets of medical images. Then, the two best networks were selected according to the accuracy and efficiency of the segmentation. One of the main results of the study was the selection of the two best neural networks that provide the most accurate segmentation of vertebrae. Next, the ensemble method was applied, based on the averaging of the predictions of the selected networks. This approach made it possible to improve the overall accuracy of determining the diagnostic parameters of the spine. The obtained results emphasize the effectiveness of using an ensemble of neural networks in the context of medical segmentation. Ensembles provide more stable and accurate predictions by reducing the impact of random errors of individual networks. Ensemble predictions of these networks lead to a statistically significant improvement in results compared to individual approaches. This is an important step in the direction of creating reliable systems of automated diagnostics capable of helping doctors in conducting more accurate and operative analyses.


Keywords: machine learning; neural networks; deep learning; image segmentation; medical image analysis.


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Received 20 February 2024

Published 30 March 2024