Alzheimer Disease Classification Model Using Machine Learning Techniques

  • Authors

    • Ajay N. Upadhyaya Professor, Department of Computer Engineering, SAL Engineering & Technical Institute, SAL Education, Ahmedabad-380060, Gu-jarat, ‎India
    • W. Grace Shanthi Department of Mathematics and Humanities, Kakatiya Institute of Technology and Science, Warangal, India
    • Raja J Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai-600062, India
    • K.R. Prasanna Kumar Department of Computer Science and Design, Kongu Engineering College, Erode 638060, India
    • Mahesh Babu Kota Department of Electronics and Communication Engineering, Aditya University, Surampalem, Kakinada -533437, India
    • Sharmiladevi B. Department of Electrical and Electronics Engineering, M.Kumarasamy College of Engineering, Thalavapalayam, Karur, ‎Tamil Nadu, India.
    • Samson Isaac Division of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
    https://doi.org/10.14419/rcsfqr61

    Received date: April 15, 2025

    Accepted date: June 29, 2025

    Published date: July 28, 2025

  • Machine Learning; Convolutional Neural Networks; MRI Brain Images; Image Classification

  • Abstract

    Technological advances have allowed the machine learning area of the field of artificial intelligence to emerge and provide new ‎solutions to different problems. Medicine is one of the sciences driving these advanced solutions. Using real-time data, a model ‎has been developed for the classification of brain images taken through neuroimaging techniques to assist in the diagnosis of ‎Alzheimer's disease (AD). Alzheimer's is a very important condition as it affects cognitive functions and daily living activities. ‎For the development of this work, convolutional neural networks were used with a total of 5,600 images for training and testing; ‎the images were extracted from medical reports. In the end, an accuracy level of 70% was reached in all cases during training ‎and evaluation. This percentage was considered acceptable by the medical specialist‎.

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  • How to Cite

    Upadhyaya, A. N. . ., Shanthi , W. G. ., J , R., Kumar , K. P. ., Kota , M. B. ., B. , S. ., & Isaac , S. . (2025). Alzheimer Disease Classification Model Using Machine Learning Techniques. International Journal of Basic and Applied Sciences, 14(3), 396-405. https://doi.org/10.14419/rcsfqr61