Alzheimer’s Disease Classification Based on Transfer Learning of New-CNN Model

Year : 2025 | Volume : 15 | Issue : 03 | Page : 16 23
    By

    Aniket Malik,

  • Prabhjot Kaur,

  1. Researcher, Department of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
  2. Assistant Professor, Department of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

Abstract

The long-term, irreversible brain disorder “Alzheimer’s disease (AD)” currently has no known cure. Nonetheless, current medications may impede their advancement. Globally, those over 65 are the primary population affected by Alzheimer’s disease. Accurate detection of this condition requires early diagnosis. Because there are so many people who come with an ailment, manual diagnosis by health specialists is laborious and prone to error. Early detection of AD is a difficult undertaking in clinical practice and only very few diagnostic techniques are available. To prevent brain damage and arrest the disease’s progression, patients may benefit from early detection of mild cases, which can help direct further medical care. “Deep learning (DL)” has attracted attention lately as an early AD identification method. Several scientists and academics are working to develop techniques for early identification utilizing MRI pictures to halt the progression of AD. In this paper, 6400 images are gathered from secondary sources for the current investigation. Preprocessing is done using the normalization procedure. CNN is fed these preprocessed, normalized images. CNN’s training and testing accuracy are improved by the normalized images. CNN obtains 94.89% classification accuracy for AD in image testing. Furthermore, the outcome demonstrates that the pre-trained New-CNN model performs better during transfer learning.

Keywords: Alzheimer disease (AD), classification, convolutional neural network (CNN), detection, deep learning (DL), brain disease

[This article belongs to Research and Reviews: A Journal of Neuroscience ]

How to cite this article:
Aniket Malik, Prabhjot Kaur. Alzheimer’s Disease Classification Based on Transfer Learning of New-CNN Model. Research and Reviews: A Journal of Neuroscience. 2025; 15(03):16-23.
How to cite this URL:
Aniket Malik, Prabhjot Kaur. Alzheimer’s Disease Classification Based on Transfer Learning of New-CNN Model. Research and Reviews: A Journal of Neuroscience. 2025; 15(03):16-23. Available from: https://journals.stmjournals.com/rrjons/article=2025/view=233986


References

  1. Goenka N, Tiwari S. AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches. Biomed Signal Process Control. 2022 Apr;74:103500.
  2. Sharma S, Guleria K, Sobti R. An early detection of Parkinson’s disease using machine learning and deep learning models. In: 2024 International Conference on Computational Intelligence and Computing Applications (ICCICA); 2024 May 23; Vol. 1. p. 154–9. IEEE.
  3. Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H. Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst. 2018 May;42:1.
  4. Fathi S, Ahmadi M, Dehnad A. Early diagnosis of Alzheimer’s disease based on deep learning: A systematic review. Comput Biol Med. 2022 Jul;146:105634.
  5. Fang X, Liu Z, Xu M. Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer’s disease diagnosis. IET Image Process. 2020 Feb;14(2):318–26.
  6. Mujahid M, Rehman A, Alam T, Alamri FS, Fati SM, Saba T. An efficient ensemble approach for Alzheimer’s disease detection using an adaptive synthetic technique and deep learning. Diagnostics. 2023 Jul 26;13(15):2489.
  7. Ibrahim R, Ghnemat R, Abu Al-Haija Q. Improving Alzheimer’s disease and brain tumor detection using deep learning with particle swarm optimization. AI. 2023 Jul 28;4(3):551–73.
  8. Thangavel P, Natarajan Y, Preethaa KS. EAD-DNN: Early Alzheimer’s disease prediction using deep neural networks. Biomed Signal Process Control. 2023 Sep;86:105215.
  9. Singh A, Kumar R. Brain MRI image analysis for Alzheimer’s disease (AD) prediction using deep learning approaches. SN Comput Sci. 2024 Jan 7;5(1):160.
  10. Balaji P, Chaurasia MA, Bilfaqih SM, Muniasamy A, Alsid LE. Hybridized deep learning approach for detecting Alzheimer’s disease. Biomedicines. 2023 Jan 6;11(1):149.
  11. Marwa EG, Moustafa HE, Khalifa F, Khater H, AbdElhalim E. An MRI-based deep learning approach for accurate detection of Alzheimer’s disease. Alex Eng J. 2023 Jan 15;63:211–21.
  12. Arafa DA, Moustafa HE, Ali HA, Ali-Eldin AM, Saraya SF. A deep learning framework for early diagnosis of Alzheimer’s disease on MRI images. Multimed Tools Appl. 2024 Jan;83(2):3767–99.
  13. Rajesh Khanna M. Multi-level classification of Alzheimer disease using DCNN and ensemble deep learning techniques. Signal Image Video Process. 2023 Oct;17(7):3603–11.
  14. Yellu RR, Kukalakunta Y, Thunki P. Deep learning-assisted diagnosis of Alzheimer’s disease from brain imaging data. J AI Healthc Med. 2024 May 1;4(1):36–44.
  15. El-Latif AA, Chelloug SA, Alabdulhafith M, Hammad M. Accurate detection of Alzheimer’s disease using lightweight deep learning model on MRI data. Diagnostics. 2023 Mar 23;13(7):1216.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 27/05/2025
Accepted 02/07/2025
Published 09/12/2025
Publication Time 196 Days
57

Login


My IP

PlumX Metrics