Using convolutional neural networks in solving problems of image analysis and recognition


  • Aigerim Astanayeva Al-Farabi Kazakh National University
  • Ainur Kozbakova Institute of Information and Computational Technologies CS MES RK


Keywords: filter, convolution, neural networks, architecture, deep learning, convolutional neural networks


Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition.

Тhe purpose of the work, the results of which are presented in the article, was to research of modern architectures of convolutional neural networks for image recognition. The article discusses such architectures as AlexNet, ZFnet, VGGNet, GoogleNet, ResNet. Based on the results obtained, it was revealed that at the moment the network with the most accurate result is the ResNet convolutional network with an accuracy rate of 3.57%. The advantage of this research is that the given article gives a brief description of the convolutional neural network, and also gives an idea of the modern architectures of convolutional networks, their structure and quality indicators.


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How to Cite

Astanayeva, A., & Kozbakova, A. (2021). Using convolutional neural networks in solving problems of image analysis and recognition. ADVANCED TECHNOLOGIES AND COMPUTER SCIENCE, (1), 27–33. Retrieved from



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