Decision Making Support System for the Diagnostics of the Cardiovascular System Pathologies by the X-ray Images of the Chest
https://doi.org/10.35596/1729-7648-2023-21-1-98-103
Abstract
The lack of universal (generalized) data sets, as well as the lack of annotated data, creates the need to study the possibilities of neural network approaches for specific data sets. The importance of building algorithms for detecting extrapulmonary pathologies on chest X-ray images is dictated by the great social significance of many diseases of this group (for example, cardiovascular diseases), given the availability of such images, due to the widespread use of minimally invasive and relatively cheap X-ray diagnostic methods. One of the most impor tant issues in solving the problems of automating the classification of medical images is data preparation. As a result of work on the image base, the performance of the final algorithm has been increased from 75 to 95 %. The processing of the entire volume of the obtained images and their diagnostics for a wide list of pathologies are difficult for medical institutions because of the limited resources. In this regard, it is advisable to use the automation of segmentation and recognition processes, which even at the first stages of development of the technology makes it possible to redistribute the attention of doctors, focusing on potentially pathological cases and returning attention to cases mistakenly identified as non-pathological.
About the Author
A. G. Radzhabov
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Radzhabov Ahmedkhan Gadgimammyaevich, Postgraduate, Junior Researcher
220141, Russiyanova St., 50
+375 33 385-23-20
References
1. Kanyukov V. N. (2009) Computer Technologies in Biomedical Research. Ch. 1. Orenburg, GOU ORG. 109. (in Russian).
2. Shortiffe E. H. (1976) Computer-Based Medical Consultations: MYCIN. New York, Elsevier Computer Science Publi. Comp. 286.
3. Pekker Ya. S. (2002) Computer Technologies in Biomedical Research. Biological Signals and Medical Imaging. Tomsk, Tomsk Polytechnic University Publ. 240 (in Russian).
4. Gudfellou Ya. D. (2017) Deep Learning. Saint Petersburg, DMK. 652.
5. Bustos A., Pertusa A., Salinas J.-M., de la Iglesia-Vayá M. (2020) PadChest: a Large Chest X-ray Image Dataset with Multi-label Annotated Reports. Medical Image Analysis. 66.
6. Han Y., Glicksberg B. (2021) Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-Rays with Radiomics Using a Feedback Loop. arXiv. https://arxiv.org/pdf/2104.04968.pdf (Accessed 25 November 2021).
7. Sowrirajan H., Yang J., Ng A. Y., Rajpurkar P. (2021) MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-Ray Models. arXiv. https://arxiv.org/pdf/2010.05352.pdf (Accessed 25 November 2021).
8. Chen X., Shi B. (2018) Deep Mask for X-Ray Based Heart Disease Classification. arXiv. https://arxiv.org/abs/1808.08277 (Accessed 25 November 2021).
9. Radzhabov A., Kovalev V. (2021) Performance Analysis of Deep Learning Models for Heart Segmentation in Chest X-ray Images on a Small Dataset. International Conference on Pattern Recognition and Information Processing.
10. Tan M., Le Q. V. (2020) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv. https://arxiv.org/pdf/1905.11946.pdf (Accessed 25 November 2021).
Review
For citations:
Radzhabov A.G. Decision Making Support System for the Diagnostics of the Cardiovascular System Pathologies by the X-ray Images of the Chest. Doklady BGUIR. 2023;21(1):98-103. (In Russ.) https://doi.org/10.35596/1729-7648-2023-21-1-98-103
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