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All Journal International Journal of Electrical and Computer Engineering
Kaibassova, Dinara
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Computer Science & IT Electrical & Electronics Engineering

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Using deep learning to diagnose retinal diseases through medical image analysis Azhibekova, Zhanar; Bekbayeva, Roza; Yussupova, Gulbakhar; Kaibassova, Dinara; Ostretsova, Idiya; Muratbekova, Svetlana; Kakabayev, Anuarbek; Sultanova, Zhanylsyn
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6455-6465

Abstract

The scientific article focuses on the application of deep learning through simple U-Net, attention U-Net, residual U-Net, and residual attention U-Net models for diagnosing retinal diseases based on medical image analysis. The work includes a thorough analysis of each model's ability to detect retinal pathologies, taking into account their unique characteristics such as attention mechanisms and residual connections. The obtained experimental results confirm the high accuracy and reliability of the proposed models, emphasizing their potential as effective tools for automated diagnosis of retinal diseases based on medical images. This approach opens up new prospects for improving diagnostic procedures and increasing the efficiency of medical practice. The authors of the article propose an innovative method that can significantly facilitate the process of identifying retinal diseases, which is critical for early diagnosis and timely treatment. The results of the study support the prospect of using these models in clinical practice, highlighting their ability to accurately analyze medical images and improve the quality of eye health care.
Human-machine interactions based on hand gesture recognition using deep learning methods Zholshiyeva, Lazzat; Manbetova, Zhanat; Kaibassova, Dinara; Kassymova, Akmaral; Tashenova, Zhuldyz; Baizhumanov, Saduakas; Yerzhanova, Akbota; Aikhynbay, Kulaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp741-748

Abstract

Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN), which show excellent results in gesture recognition tasks. Next, the development and implementation of a human-machine interaction system based on hand gesture recognition is discussed. System architectures are described, as well as technical and practical aspects of their application. In conclusion, the article summarizes the research results and outlines the prospects for the development of hand gesture recognition technology to improve human-machine interaction. The advantages and limitations of the technology are analyzed, as well as possible areas of its application in the future.
Co-Authors Aikhynbay, Kulaisha Azhibekova, Zhanar Baizhumanov, Saduakas Bekbayeva, Roza Kakabayev, Anuarbek Kassymova, Akmaral Manbetova, Zhanat Muratbekova, Svetlana Ostretsova, Idiya Sultanova, Zhanylsyn Tashenova, Zhuldyz Yerzhanova, Akbota Yussupova, Gulbakhar Zholshiyeva, Lazzat