Abstract: Alzheimer’s Disease (AD) is a terminal illness affecting the human brain that leads to deterioration of cognitive function and should therefore be diagnosed as early as possible. The goal of this work is to come up with a precise and interpretable diagnostic model for the early diagnosis of Alzheimer's Disease (AD) based on multi-modal neuroimaging data. Current deep learning models such as Convolutional Neural Networks (CNNs) are limited in that they lose spatial hierarchies in 3D medical images, which inhibits classification performance and interpretability. To overcome this, in this work, we introduce a new 3D Capsule Network (3D-CapsNet) framework that captures spatial relations more effectively with dynamic routing and pose encoding to improve volumetric neuroimaging data analysis. Our approach has three principal phases: extensive pre-processing of MRI and PET scans such as skull stripping, intensity normalization, and motion correction; feature extraction through the 3D-CapsNet model; and multi-modal classification based on fusion. We used the Alzheimer's Classification dataset from Kaggle for training and testing. The model is implemented in the Python platform with TensorFlow and Keras libraries incorporating 3D CNN operations along with capsule layers to extract fine-grained features of AD-affected brain areas such as the hippocampus and entorhinal cortex. Experimental results show that our model reaches a very high classification accuracy of 92%, which is higher than the conventional architectures VGG-16, ResNet-50, and DenseNet-121 in accuracy, precision, recall, F1-score, and AUC-ROC. This strategy is helpful to clinicians and medical researchers because it gives them a non-invasive, interpretable, and trustworthy tool for diagnosing and monitoring various stages of AD (Non-Demented, Very Mild, Mild, and Moderate). It sets the stage for real-time clinical integration and future studies in monitoring disease progression over time.

Kabilan Annadurai, A Suresh Kumar, Yousef A.Baker El-Ebiary, Sachin Upadhye, Janjhyam Venkata Naga Ramesh, K. Lalitha Vanisree and Elangovan Muniyandy, “Capsule Network-Based Multi-Modal Neuroimaging Approach for Early Alzheimer’s Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160557

@article{Annadurai2025,
title = {Capsule Network-Based Multi-Modal Neuroimaging Approach for Early Alzheimer’s Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160557},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160557},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Kabilan Annadurai and A Suresh Kumar and Yousef A.Baker El-Ebiary and Sachin Upadhye and Janjhyam Venkata Naga Ramesh and K. Lalitha Vanisree and Elangovan Muniyandy}
}