Intelligent MRI-Based Neurocognitive Stage Classification System Using Custom CNN for Alzheimer’s Disease Diagnosis
DOI:
https://doi.org/10.15662/IJEETR.2026.0802160Keywords:
Alzheimer’s Disease, Brain MRI, Convolutional Neural Network, Deep Learning, Medical Image Classification, Explainable AI, Early DetectionAbstract
The increasing prevalence of Alzheimer’s disease (AD), a progressive neurodegenerative disorder that impairs memory, cognition, and daily functioning, has created an urgent need for accurate and early-stage diagnostic systems. Traditional clinical diagnosis often depends on manual interpretation of brain scans, which can be time-consuming and prone to variability. In response, Artificial Intelligence (AI), particularly Deep Learning (DL), has emerged as a powerful tool for automated medical image analysis and disease stage classification.
This paper presents an intelligent MRI-based Alzheimer’s disease stage classification system using a compact Convolutional Neural Network (CNN). The proposed framework is designed to classify brain MRI scans into four clinically significant categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. To improve model robustness and generalization, multiple preprocessing and data augmentation techniques were applied to enhance image quality and increase dataset diversity.
A lightweight four-layer CNN architecture was developed to automatically extract discriminative spatial features from MRI images while maintaining computational efficiency. The dataset was systematically divided into training, validation, and testing subsets to ensure reliable and unbiased performance evaluation. Experimental analysis demonstrates that the proposed model achieved a classification accuracy of 99.07%, indicating strong capability in recognizing previously unseen MRI scans.
To improve the transparency and trustworthiness of the system, heatmap-based visualization techniques were incorporated to highlight the brain regions contributing to model predictions. Furthermore, a user-friendly web-based interface was developed to enable easy MRI image upload and instant disease stage prediction. The findings suggest that the proposed AI-driven framework can serve as an effective decision-support tool for healthcare professionals in the early detection and progression analysis of Alzheimer’s disease.
References
1. C. M. Chabib, L. J. Hadjileontiadis, and A. Al Shehhi,“DeepCurvMRI: Deep convolutional curvelet transform-based MRI approach for early detection of Alzheimer’s disease,” IEEE Access, vol. 11, pp. 47263–47275, 2023.
2. B. Khagi and G.-R. Kwon, “3D CNN design for the classification of Alzheimer's disease using brain MRI and PET,” IEEE Access, vol. 8, pp. 217550–217569, 2020.
3. S. Al-Shoukry, T. H. Rassem, and N. M. Makbol, “Alzheimer's diseases detection by using deep learning algorithms: A mini-review,” IEEE Access, vol. 8, pp. 77131–77141, 2020.
4. K. Velu and N. Jaisankar, “Design of a CNN–Swin transformer model for Alzheimer’s disease prediction using MRI images,” IEEE Access, 2024.
5. E. Kina, “TLEABLCNN: Brain and Alzheimer’s disease detection using attention-based explainable deep learning and SMOTE using imbalanced brain MRI,” IEEE Access, vol. 13, 2025.
6. A. Basher et al., “Volumetric feature-based Alzheimer's disease diagnosis from sMRI data using a CNN and DNN,” IEEE Access, vol. 9, pp. 31607–31620, 2021.
7. Sugumar, R. (2025). Designing Resilient and Scalable Cloud-Native Frameworks for Generative AI Content Production. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13268-13279.
8. Bhatnagar, G., Rajoria, Y. K., Sakeel, M., Vigenesh, M., Premananthan, G., & Dongre, D. (2023, September). IoT malware detection tool with CNN classification for small devices. In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 6, pp. 2017-2023). IEEE.
9. Mathew, A., & Alex, H. (2025). Federated Learning for Secure Genomic Research: Privacy-Preserving AI Solutions for Precision Medicine. Science and Technology: Developments and Applications Vol. 9, 36-43.
10. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
11. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
12. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
13. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
14. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
15. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
16. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
17. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
18. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
19. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
20. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
21. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
22. S. Tanveer, H. S. Sardar, A. U. Haq, and M. A. M. Khan, “Machine learning techniques for the diagnosis of Alzheimer’s disease: A review,” ACM Computing Surveys, vol. 54, no. 2, pp. 1–35, 2021.
23. Y. Gao, “Alzheimer’s disease diagnosis based on PET and MRI multimodal imaging using deep learning,” Journal of Alzheimer’s Disease, vol. 80, no. 3, pp. 1015–1027, 2021.
24. H. Basaia, F. Agosta, L. Wagner, et al., “Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks,” NeuroImage: Clinical, vol. 21, 101645, 2019.
25. R. Suk, S.-W. Lee, D. Shen, and Alzheimer’s Disease Neuroimaging Initiative, “Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis,” NeuroImage, vol. 101, pp. 569–582, 2014.
26. J. Liu, M. Liu, D. Zhang, and D. Shen, “Early diagnosis of Alzheimer’s disease based on deep learning,” Proceedings of the IEEE International Conference on Biomedical and Health Informatics (BHI), pp. 1–4, 2014.
27. M. Sarraf and G. Tofighi, “DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI,” bioRxiv, 2016.
28. M. Mamun, S. B. Shawkat, M. S. Ahammed, M. M. Uddin, M. I. Mahmud, and A. M. Islam, “Deep learning based model for Alzheimer’s disease detection using brain MRI images,” in Proc. IEEE, 2022.
29. F. M. J. M. Shamrat et al., “AlzheimerNet: An effective deep learning based proposition for Alzheimer’s disease stages classification from functional brain changes in magnetic resonance images,” IEEE Access, vol. 11, pp. 16376–16390, 2023.
30. A. B. Tufail, Y. K. Ma, and Q. N. Zhang, “Binary classification of Alzheimer’s disease using sMRI imaging modality and deep learning,” Journal of Digital Imaging, vol. 33, pp. 1073–1090, 2020.
31. A. M. El-Assy et al., “A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data,” Scientific Reports, vol. 14, Art. no. 3463, 2024.
32. A. Alasiry et al., “A novel neuroimaging based early detection framework for Alzheimer disease using deep learning,” Scientific Reports, vol. 15, Art. no. 23011, 2025.
33. M. G. Alsubaie, S. Luo, and K. Shaukat, “Alzheimer’s disease detection using deep learning on neuroimaging: A systematic review,” Machine Learning and Knowledge Extraction, vol. 6, no. 1, pp. 464–505, 2024.
34. S. Mohsen, “Alzheimer’s disease detection using deep learning and machine learning: A review,” Artificial Intelligence Review, vol. 58, Art. no. 262, 2025.
35. Z. Ur Rehman et al., “Deep learning techniques for Alzheimer’s disease detection in 3D imaging: A systematic review,” Health Science Reports, vol. 7, no. 9, e70025, 2024.
36. I. Malik et al., “Deep learning for Alzheimer’s disease prediction: A comprehensive review,” Diagnostics, vol. 14, no. 12, Art. no. 1281, 2024.
37. A. D. Arya et al., “A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease,” Brain Informatics, vol. 10, Art. no. 17, 2023.
38. N. S. N. Ibrahim et al., “Comparing deep learning CNN method with traditional MRI-based hippocampal segmentation and volumetry for early Alzheimer’s disease diagnosis across diverse populations,” Scientific Reports, vol. 16, Art. no. 58, 2026.
39. M. Rallabandi, K. Tulpule, and M. Gattu, “Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer’s disease using structural MRI analysis,” Informatics in Medicine Unlocked, vol. 18, Art. no. 100305, 2020.
40. S. Murugan et al., “DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images,” IEEE Access, vol. 9, pp. 90319–90329, 2021.
41. J.-H. Noh, J.-H. Kim, and H.-D. Yang, “Classification of Alzheimer’s progression using fMRI data,” Sensors, vol. 23, no. 14, Art. no. 6330, 2023.
42. S. A. Javid and M. M. Feghhi, “Early diagnosis of Alzheimer’s disease from MRI images with deep learning model,” arXiv preprint, arXiv:2409.18814, 2024.
43. S. Odimayo et al., “SNeurodCNN: Structure-focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer’s disease,” arXiv preprint, arXiv:2401.03922, 2024.
44. A. H. Rashid et al., “Biceph-Net: A robust and lightweight framework for the diagnosis of Alzheimer’s disease using 2D-MRI scans and deep similarity learning,” arXiv preprint, arXiv:2203.12197, 2022.
45. S. Sarraf and G. Tofighi, “Classification of Alzheimer’s disease structural MRI data by deep learning convolutional neural networks,” arXiv preprint, arXiv:1607.06583, 2016.
46. S. Bamber and T. Vishvakarma, “Medical image classification for Alzheimer’s using a deep learning approach,” Journal of Engineering and Applied Science, vol. 70, no. 1, Art. no. 54, 2023.
47. A. W. Salehi, P. Baglat, and G. Gupta, “Alzheimer’s disease diagnosis using deep learning techniques,” International Journal of Engineering and Advanced Technology, vol. 9, no. 3, pp. 874–880, 2020.
48. M. Ahmadzadeh et al., “Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to Alzheimer’s disease: A systematic review,” BMC Neurology, vol. 23, Art. no. 309, 2023.
49. F. M. J. M. Shamrat et al., “AlzheimerNet: An effective deep learning based proposition for Alzheimer’s disease stages classification,” IEEE Access, vol. 11, pp. 16376–16390, 2023.
50. J. Wen et al., “Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation,” Medical Image Analysis, vol. 63, Art. no. 101694, 2020.
