Multi-filter stacking in inception V3 for enhanced Alzheimer's severity classification

Alzheimer's disease, a progressive neurodegenerative disorder, is characterized by a decline in brain volume and neuronal loss, with early symptoms often presenting as short-term memory impairment. Automated classification of Alzheimer's disease remains a significant challenge due to inter-patient variability in brain morphology, aging effects, and overlapping anatomical features across different stages. While traditional machine learning techniques, such as Support Vector Machines (SVMs) and various Deep Neural Network (DNN) models, have been explored, the need for more accurate and efficient classification techniques persists. In this study, we propose a novel approach that integrates Multi-Filter Stacking with the Inception V3 architecture, referred to as CASFI (Classifying Alzheimer's Severity using Filter Integration). This method leverages diverse convolutional filter sizes to capture multiscale spatial features, enhancing the model's ability to detect subtle structural variations associated with different Alzheimer's disease stages. Applied to MRI data, CASFI achieved an accuracy of 97.27%, outperforming baseline deep learning models and traditional classifiers in both accuracy and robustness. This approach supports early diagnosis and informed clinical decision-making, providing a valuable tool to assist healthcare professionals in managing and planning treatment for Alzheimer's patients.