System analysis and model of disease identification based on medical images

Authors

  • Yu.O. Ushenko Chernivtsi National University named after Yu. Fedkovicha
  • D.I. Uhryn Chernivtsi National University named after Yu. Fedkovicha
  • O.V. Galochkin Chernivtsi National University named after Yu. Fedkovicha
  • I.V. Zosko Chernivtsi National University named after Yu. Fedkovicha

DOI:

https://doi.org/10.31649/1681-7893-2022-44-2-93-99

Keywords:

medical images, machine learning, identification of diseases, neural networks

Abstract

In given article, we investigate medical images and develop an intelligent system for identification of the disease on their basis. The paper proposes an approach to finding the affected tissue areas in medical images. To find them, a mask was extracted for training a neural network. Mask extraction was carried out using annotations, where polygons with affected tissues were identified. The studied objects were assigned to different classifications of morbidity.

Author Biographies

Yu.O. Ushenko, Chernivtsi National University named after Yu. Fedkovicha

doctor of physical and mathematical sciences, professor, head of computer sciences, Chernivtsi National University named after Yu. Fedkovicha, Chernivtsi

D.I. Uhryn, Chernivtsi National University named after Yu. Fedkovicha

Doctor of Technical Sciences, Associate Professor of the Department of Computer Sciences, Chernivtsi National University named after Yu. Fedkovicha, Chernivtsi

O.V. Galochkin, Chernivtsi National University named after Yu. Fedkovicha

candidate of technical sciences, assistant of the department of computer sciences, Chernivtsi National University named after Yu. Fedkovicha, Chernivtsi

I.V. Zosko , Chernivtsi National University named after Yu. Fedkovicha

Master's student of the Department of Computer Sciences,
Chernivtsi National University named after Yu. Fedkovicha, Chernivtsi

References

Springer.com [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://link.springer.com/article/10.1007/s13735-021-00218-1 (accessed 12/20/2022).

Springer.com [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Mode of access:https://link.springer.com/article/10.1007/s13735-021-00218-1#ref-CR2(application date 12/20/2022).

Sciencedirect.com [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Mode of access:http://sciencedirect.com/science/article/pii/S1877050919321295(application date 12/20/2022).

Science.org [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Mode of access:https://spj.science.org/doi/10.34133/2021/8786793(application date 12/20/2022).

Glassboxmedicine.com [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Mode of access:https://glassboxmedicine.com/2020/01/21/segmentation-u-net-mask-r-cnn-and-medical-applications/(application date 12/20/2022).

Nature.com[Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Mode of access:https://www.nature.com/articles/s41746-022-00592-y(application date 12/20/2022).

Frontiersin.org [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Mode of access:https://www.frontiersin.org/articles/10.3389/fnins.2021.714318/full(application date 12/20/2022).

Evergreens.com.ua [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://evergreens.com.ua/ua/articles/cnn.html (access date 12/20/2022).

Paperswithcode.com [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://paperswithcode.com/method/u-net (access date 12/20/2022).

Geeksforgeeks.org [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://www.geeksforgeeks.org/residual-networks-resnet-deep-learning/ (accessed 12/20/2022).

Arxiv.org[Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Mode of access:https://arxiv.org/abs/2212.05961(application date 12/20/2022).

Github.com[Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://github.com/ahmedfgad/Mask-RCNN-TF2 (accessed 12/20/2022).

Robots.ox.ac.uk [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://www.robots.ox.ac.uk/~vgg/publications/2021/Albanie21a/albanie21a.pdf (accessed 12/20/2022).

Kikaben.com [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://kikaben.com/r-cnn-original/ (access date 12/20/2022).

hindawai.com [Electronic resource]: [Internet portal]. – Electronic data. – [Article] – Access mode: https://www.hindawi.com/journals/jhe/2022/9580991/ (access date 12/20/2022).

Downloads

Abstract views: 148

Published

2023-01-20

How to Cite

[1]
Y. . Ushenko, D. . Uhryn, O. . Galochkin, and I. . Zosko, “System analysis and model of disease identification based on medical images ”, Опт-ел. інф-енерг. техн., vol. 44, no. 2, pp. 93–99, Jan. 2023.

Issue

Section

Biomedical Optical And Electronic Systems And Devices

Metrics

Downloads

Download data is not yet available.

Most read articles by the same author(s)

1 2 > >>