CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW

Authors

  • Azhar Toilybaikyzy Tursynova al-Farabi KazNU
  • B. S. Omarov
  • O. A. Postolache
  • M. Zh. Sakypbekova

DOI:

https://doi.org/10.26577/JMMCS.2021.v112.i4.09

Keywords:

artificial intelligence, deep learning, stroke, convolutional neural network, MRI, CT

Abstract

Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN). Convolutional neural networks have dierent research issues and medicine is one of the main ones. Today, the predominant global problem is acute cerebral blood ow disorder - stroke. The most important diagnostic tests for stroke are computerized tomography (CT) imaging and magnetic resonance imaging (MRI). However, late recognition and diagnosis by a specialist can aect the lives of many patients. For such cases, the role and help of the convolutional neural networks is extraordinary. In-depth clustering neural networks apply non-linear transformations and abstractions of highlevel models in large databases. Year after year, advances in the eld of deep learning architecture, namely crate neural networks for the recognition of stroke, are making a signicant contribution to the development of medicine. The given article aords a review of the achievements of deep learning neural networks in the recognition of stroke from brain images. The next review chronologically presents the main neural network block diagram and open databases providing MRI and CT images. In addition, a comparative analysis of the use of convolutional neural networks in the detection of stroke is presented, as well as the achieved indicators of the methodologies used.

References

[1] Deep Learning: review, accessed july 12, 2019, https://habr.com/ru/company/otus/blog/459785/
[2] Kim I. W., Oh J. M. Deep learning: from chemoinformatics to precision medicine //Journal of Pharmaceutical
Investigation. – 2017. – Т. 47. – №. 4. – С. 317-323.
[3] Benjamin E. J. et al. Heart disease and stroke Statistics-2020 update a report from the American Heart
Association //Circulation, vol. 141, pp. 139–596, 2020
[4] Benjamin E. J., Blaha M. J. chiuve SE, cushman M, das SR, deo R, de Ferranti Sd, Floyd J, Fornage M,
Gillespie c, et al //Heart disease and stroke statistics-2017 update: A report from the american heart association. circulation. – 2017. – Т. 135. – С. e146-e603.
[5] Xu J. et al. Mortality in the United States, 2012. – US Department of Health and Human Services, Centers for
Disease Control and Prevention, National Center for Health Statistics, 2014. – №. 168.
[6] Xu J. et al. Deaths: final data for 2007 //National vital statistics reports: from the Centers for Disease Control
and Prevention, National Center for Health Statistics, National Vital Statistics System. – 2010. – Т. 58. – №. 19. – С. 1-19.
[7] Messay T., Hardie R. C., Rogers S. K. A new computationally efficient CAD system for pulmonary nodule
detection in CT imagery //Medical image analysis. – 2010. – Т. 14. – №. 3. – С. 390-406.
[8] Rodrigues da Silva E. Ambiente virtual colaborativo de diagnóstico a distância integrado a ferramentas de
manipulação de imagens : дис. – Universidade Federal de Pernambuco, 2010.
[9] Mosavi A., Varkonyi-Koczy A. R. Integration of machine learning and optimization for robot learning
//Recent Global Research and Education: Technological Challenges. – Springer, Cham, 2017. – С. 349-355.
[10] Bengio Y. Learning deep architectures for AI. – Now Publishers Inc, 2009.
[11] Feng N., Geng X., Qin L. Study on MRI Medical Image Segmentation Technology Based on CNN-CRF
Model //IEEE Access. – 2020. – Т. 8. – С. 60505-60514.
[12] Calvachi P. European Stroke 2020: Stroke detection and subtype classification using Convolutional
Neural Networks (CNNs) //Alzheimer's and Dementia. – 2020.
[13] Liu L. et al. Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in
multi-modality MRI //Neural Computing and Applications. – 2019. – С. 1-14.
[14] Tomita N. et al. Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional
neural network //NeuroImage: Clinical. – 2020. – С. 102276.
[15] Tolhuisen M. et al. A Convolutional Neural Network for Anterior Intra-Arterial Thrombus Detection and
Segmentation on Non-Contrast Computed Tomography of Patients with Acute Ischemic Stroke //Applied Sciences. – 2020. – Т. 10. – №. 14. – С. 4861.
[16] Integrating uncertainty in deep neural networks for MRI based stroke analysis accessed august 13, 2020,
https://arxiv.org/abs/2008.06332
[17] Gaidhani B. R., Rajamenakshi R. R., Sonavane S. Brain Stroke Detection Using Convolutional Neural
Network and Deep Learning Models //2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT). – IEEE, 2019. – С. 242-249.
[18] Öman O. et al. 3D convolutional neural networks applied to CT angiography in the detection of acute
ischemic stroke //European radiology experimental. – 2019. – Т. 3. – №. 1. – С. 8.
[19] Marbun J. T. et al. Classification of stroke disease using convolutional neural network //Journal of Physics
Conference Series. – 2018. – Т. 978. – №. 1. – С. 012092.
[20] Pereira D. R. et al. Stroke lesion detection using convolutional neural networks //2018 International joint
conference on neural networks (IJCNN). – IEEE, 2018. – С. 1-6. [21] Gaidhani B. R., Rajamenakshi R. R., Sonavane S. Brain Stroke Detection Using Convolutional Neural
[21] Lisowska A. et al. Context-aware convolutional neural networks for stroke sign detection in non-contrast CT
scans //Annual Conference on Medical Image Understanding and Analysis. – Springer, Cham, 2017. – С. 494-505.
[22] Lisowska A. et al. Thrombus detection in ct brain scans using a convolutional neural network //International
Conference on Bioimaging. – SCITEPRESS, 2017. – Т. 3. – С. 24-33.
[23] Chen L., Bentley P., Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using
convolutional neural networks //NeuroImage: Clinical. – 2017. – Т. 15. – С. 633-643.

Downloads

Published

2021-12-31