Authors : Annappa S. S.; Asha S.; Lohith C.

Volume/Issue : Volume 11 - 2026, Issue 7 - July

---

Google Scholar : https://tinyurl.com/akt7u6j4

Scribd : https://tinyurl.com/yeynpahm

DOI : https://doi.org/10.38124/ijisrt/26jul1280

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : imely and effective treatment of Alzheimer's disease (AD) depends heavily on early detection. This work proposes a hybrid Vision Transformer (ViT) and Deep Belief Network (DBN) model, metaheuristically optimized for early AD detection from brain MRI images. Prior to classification, an Adaptive Cancellation Filter (ACF) is applied to eliminate noise and enhance structural features. The DBN–ViT architecture enables effective hierarchical feature learning while jointly capturing long-range and local dependencies within the imaging data. To improve convergence and stability, the Chimp Optimization Algorithm (ChOA) is used to optimize the model's parameters.

Keywords : Vision Transformer (ViT); Deep Belief Network (DBN); Adaptive Cancellation Filter (ACF); Chimp Optimization Algorithm (ChOA).

References :

1. H. A. Helaly, M. Badawy, and A. Y. Haikal, "Deep learning approach for early detection of Alzheimer's disease," Cognitive Computation, vol. 14, no. 5, pp. 1711–1727, 2022.
2. S. Liu, A. V. Masurkar, H. Rusinek, J. Chen, B. Zhang, W. Zhu, C. Fernandez-Granda, and N. Razavian, "Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs," Scientific Reports, vol. 12, no. 1, p. 17106, 2022.
3. A. G. Vrahatis, K. Skolariki, M. G. Krokidis, K. Lazaros, T. P. Exarchos, and P. Vlamos, "Revolutionizing the early detection of Alzheimer's disease through non-invasive biomarkers: The role of artificial intelligence and deep learning," Sensors, vol. 23, no. 9, p. 4184, 2023.
4. J. Venugopalan, L. Tong, H. R. Hassanzadeh, and M. D. Wang, "Multimodal deep learning models for early detection of Alzheimer's disease stage," Scientific Reports, vol. 11, no. 1, p. 3254, 2021.
5. B. Khagi, K. H. Lee, K. Y. Choi, J. J. Lee, G. R. Kwon, and H. D. Yang, "VBM-based Alzheimer's disease detection from the region of interest of T1 MRI with supportive Gaussian smoothing and a Bayesian regularized neural network," Appl. Sci., vol. 11, no. 13, p. 6175, 2021.
6. A. B. Tufail, Y. K. Ma, M. K. A. Kaabar, A. U. Rehman, R. Khan, and O. Cheikhrouhou, "Classification of initial stages of Alzheimer's disease through PET neuroimaging modality and deep learning: Quantifying the impact of image filtering approaches," Mathematics, vol. 9, no. 23, p. 3101, 2021.
7. K. AlSharabi, Y. Bin Salamah, A. M. Abdurraqeeb, M. Aljalal, and F. A. Alturki, "EEG signal processing for Alzheimer's disorders using discrete wavelet transform and machine learning approaches," IEEE Access, vol. 10, pp. 89781–89797, 2022.
8. M. J. Oliveira, P. Ribeiro, and P. M. Rodrigues, "Machine learning in Alzheimer's disease diagnosis," Bioengineering, vol. 11, p. 1153, 2024.
9. G. Pahuja and T. N. Nagabhushan, "A novel GA-ELM approach for Parkinson's disease detection using brain structural T1-weighted MRI data," in Proc. 2nd Int. Conf. Cognitive Computing and Information Processing (CCIP), IEEE, 2016.
10. A. Elaraby, W. Hamdy, and M. Alruwaili, "Optimization of deep learning model for plant disease detection using particle swarm optimizer," Computers, Materials & Continua, vol. 71, no. 2, 2022.
11. J. Wu, R. Tao, P. Zhao, N. F. Martin, and N. Hovakimyan, "Optimizing nitrogen management with deep reinforcement learning and crop simulations," in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2022.
12. M. Odusami, R. Maskeliūnas, R. Damaševičius, and T. Krilavičius, "Analysis of features of Alzheimer's disease: Detection of early stage from functional brain changes in magnetic resonance images using a fine-tuned ResNet18 network," Diagnostics, vol. 11, no. 6, p. 1071, 2021.