CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW

Tursynova A.T. Omarov B.S. Postolache O.A. Sakypbekova M.Zh.
2021 al-Farabi Kazakh State National University

KazNU Bulletin. Mathematics, Mechanics, Computer Science Series
2021 #112 Issue 4 109 - 115 pp.

Deep learning is one of the developing area of artificial intelligence research. It includes artificial neural network-based machine learning approaches. One method that has been widely used and researched in recent years is convolution neural networks (CNN). Convolutional neural networks have different research issues and medicine is one of the main ones. Today, the predominant global problem is acute cerebral blood flow 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 affect the lives of many patients. For such cases, the role and help of convolutional neural networks are extraordinary. In-depth clustering neural networks apply non-linear transformations and abstractions of high-level models in large databases. Year after year, advances in the field of deep learning architecture, namely crate neural networks for the recognition of stroke, are making an important contribution to medicine’s evolution. In this paper, a review of the achievements of deep learning neural networks in the recognition of stroke from brain images is considered. This review chronologically presents the main neural network block diagram and open databases providing MRI and CT images. In addition, a comparative analysis of convolutional neural networks are used in this study of stroke detection is exhibited, in addition achieved indicators of the methodologies used.

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

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Al-Farabi Kazakh National University, Almaty, Kazakhstan

Al-Farabi Kazakh National University

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