CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW - Tursynova, Azhar Toilybaikyzy; Omarov, B. S.; Postolache, O. A.; Sakypbekova, M. Zh. (Journal of Mathematics, Mechanics and Computer Science, 2021)
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.