Attention-Mechanism Deep Learning Model for Automated Detection of Alzheimer's Disease Progression from Brain Scans.

Weiguo Gee

Attention-Mechanism Deep Learning Model for Automated Detection of Alzheimer's Disease Progression from Brain Scans.

Weiguo Gee
Volume 5

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder and one of the leading causes of dementia, characterized by gradual cognitive decline, memory impairment, functional deterioration, and structural changes in the brain. Early detection of disease progression is essential for timely clinical intervention, patient monitoring, therapeutic planning, and enrollment in disease-modifying treatment trials. Neuroimaging modalities such as magnetic resonance imaging, positron emission tomography, and computed tomography provide valuable biomarkers for identifying anatomical and functional changes associated with Alzheimer’s disease. However, manual interpretation of brain scans is time-consuming, observer-dependent, and often limited in detecting subtle disease-related progression patterns during the early stages. To address these challenges, this paper proposes an attention-mechanism deep learning model for automated detection and monitoring of Alzheimer’s disease progression from brain scans.

The proposed framework integrates convolutional neural networks with attention-based feature learning to enhance the model’s ability to focus on clinically relevant brain regions affected by Alzheimer’s disease, including the hippocampus, entorhinal cortex, medial temporal lobe, posterior cingulate cortex, and cortical gray matter structures. The architecture employs spatial attention, channel attention, and self-attention modules to capture both local anatomical abnormalities and long-range dependencies across brain regions. Pre-trained deep learning backbones, including ResNet, DenseNet, EfficientNet, and Vision Transformer-based models, are adapted for three-dimensional and multi-slice neuroimaging analysis. The model is designed to classify disease stages, including cognitively normal controls, mild cognitive impairment, early Alzheimer’s disease, and advanced Alzheimer’s disease, while also supporting longitudinal progression prediction where serial imaging data are available.

A comprehensive preprocessing pipeline is applied to brain scan data, including skull stripping, bias-field correction, spatial registration, intensity normalization, brain tissue segmentation, slice selection, and anatomical region-of-interest extraction. The framework can be trained and validated using publicly available neuroimaging datasets such as the Alzheimer’s Disease Neuroimaging Initiative, OASIS, and related dementia imaging cohorts. To improve robustness and generalization, the model incorporates data augmentation, transfer learning, class-weighted loss functions, and cross-validation strategies. In longitudinal settings, temporal modeling components such as attention-guided recurrent layers or transformer encoders are integrated to learn progressive structural changes over time.

Experimental evaluation is conducted using clinically relevant performance metrics, including accuracy, sensitivity, specificity, precision, F1-score, AUC-ROC, balanced accuracy, and progression prediction error. The proposed attention-enhanced model is expected to outperform conventional CNN architectures, traditional machine learning classifiers, and non-attention-based deep learning models by improving discrimination between mild cognitive impairment and early Alzheimer’s disease, which remains one of the most clinically challenging diagnostic tasks. Furthermore, explainability techniques such as Grad-CAM, attention heatmaps, saliency mapping, and region-level attribution analysis are incorporated to visualize the brain regions contributing most strongly to the model’s predictions. These interpretability tools allow clinicians to verify whether the model focuses on neuroanatomically meaningful regions associated with Alzheimer’s disease progression.

The proposed system provides an accurate, interpretable, and clinically scalable decision-support framework for automated Alzheimer’s disease progression detection from brain scans. By combining attention-based deep learning with neuroimaging biomarkers, the framework can support early diagnosis, longitudinal monitoring, individualized risk assessment, and improved clinical decision-making. Its explainable design enhances physician trust and facilitates integration into radiology and neurology workflows, particularly in settings where rapid and objective assessment of neurodegenerative changes is required.

Keywords

References

[1] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
[2] El-kenawy, E. S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transfo rmer -
[3] Chen, L., Xu, C., Lim, W. H., Sharma, A., Tiang, S. S., Chong, K. S., ... & Khafaga, D. S. (2025). Transparent and reliable c onstruction
[4] Khodadadi, N., Towfek, S. K., Zaki, A. M., Alharbi, A. H., Khodadadi, E., Khafaga, D. S., ... & Eid, M. M. (2025). Predicting
[5] Alhussan, A. A., El -Kenawy, E. S. M., Khafaga, D. S., Alharbi, A. H., & Eid, M. M. (2025). Groundwater resource prediction and
[6] Mozhdehi, A. T., Khodadadi, N., Aboutalebi , M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
[7] Lopes, J. M., Pinho, C. S., Martins, R. C., & Bandeira, T. (2026). Sustainability Gets Smarter: Competitive Pressure and AI L eading the
[8] Radwan, M., Ibrahim, A., Abdelsalam, M. M., Alhussan, A. A., Mattar, E. A., & El -Kenawy, E. S. M. (2026). Optimizing solar and wind
[9] Alhussan, A. A., El -Kenawy, E. S. M., Eid, M. M., & Khodadadi, N. (2026). Hybrid Al -Biruni and Puma Optimization (BERPO) for
[10] El-Kenawy, E. S. M., Ibrahim, A., Alhussan, A. A., Khafaga, D. S., Ahmed, A. E., & Eid, M. M. (2026). Smart city electricity loa d
[11] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Zaki, A. M., Ibrahim, A., Alhussan, A. A., ... & Eid, M. M. (2026). Glider sn ake
[12] Mekaret, F., Rabehi, A., Zebentout, B., Tizi, S., Douara, A., Bellucci, S., ... & Alhussan, A. A. (2024). A comparative study of Schottky
[13] KOUADRI, Ali, RABEHI, Abdelhalim, BENZIANE, Ali, et al. A Robust Multi -Transform Watermarking Scheme for Medical Images
[14] Tibermacine, I. E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., Alhussan, A. A., ... & Napoli, C. (2025). Conditional VA E for
[15] Kouadri, A., Benziane, A., Rabehi, A., Rabehi, A., Alhussan, A. A., Khafaga, D. S., & El -Kenawy, E. S. M. (2025). A novel hybrid
[16] Russo, S., Tibermacine, I. E., Randieri, C., Rabehi, A., Alharbi, A. H., El -Kenawy, E. S. M., & Napoli, C. (2025). Exploiting facial
[17] Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & El -Kenawy, E. S. M. (2025). Assessment of
[18] Tibermacine, I. E., Russo, S., Citeroni, F., Mancini, G., Rabehi, A., Alharbi, A. H., ... & Napoli, C. (2025). Adversarial de noising of EEG
[19] Ouahabi, M. S., Benyounes, A., Barkat, S., Ihammouchen, S., Rekioua, T., Rabehi, A., ... & Alharbi, A. H. (2025). Real -time sensor fault
[20] Belaid, A., Guermoui, M., Khelifi, R., Arrif, T., Chekifi, T., Rabehi, A., ... & Alhussan, A. A. (2024). Assessing suitable a reas for PV
[21] Mehallou, A., M’hamdi, B., Amari, A., Teguar, M., Rabehi, A., Guermoui, M., ... & Khafaga, D. S. (2025). Optimal multiobjecti ve
[22] Rabehi, A., El -Hadi, M., Benmahmoud, S., Rabehi, A., Alharbi, A. H., & El -Kenawy, E. S. M. (2025). SOCA -CFAR Processor in A
[23] Bakria, D., Beladel, A., Korich, B., Teta, A., Mohammedi, R. D., Laouid, A. A., ... & El -kenawy, E. S. (2025). A novel enhanced Grey
[24] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
[25] Abdelhamid, A. A., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Eed, M. (2024). Potato harvesting pred iction
[26] Eed, M., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Arnous, R. (2024). Potato Consumption Forecastin g
[27] Mahmood, S., Sun, H., Iqbal, A., Alhussan, A. A., & El -kenawy, E. S. M. (2024). Green finance, sustainable infrastructure, and green
[28] El-Kenawy, E. S. M., Alhussan, A. A., Khodadadi, N., Mirjalili, S., & Eid, M. M. (2024). Predicting potato crop yield with machi ne
[29] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
[30] Ghasemi, M., Khodadadi, N., Trojovský, P., Li, L., Mansor, Z., Abualigah, L., ... & El -Kenawy, E. S. M. (2025). Kirchhoff’s law
[31] Yassen, M. A., El -Kenawy, E. S. M., Abdel -Fattah, M. G., Ismail, I., & Mostafa, H. E. D. S. (2025). Explainable artificial intelligence
[32] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning

Download