Revolutionizing Alzheimer's Detection: Hybrid Deep Learning Models in Experimental Analysis and Insights

The detection and diagnosis of Alzheimer's Disease (AD) are critically important due to the increasing prevalence of the condition, especially among aging populations. This study investigates the enhancement of AD detection through hybrid deep learning models, utilizing a dataset from Kaggle comprising images categorized into four stages: Mild Demented, Very Mild Demented, Non-Demented, and Moderate Demented. We developed two innovative models: the FusionTree Classifier (FTC) and the FusionNet Integrator (FNI). The FTC combines Random Forest and Bagging techniques to reduce variance and overfitting, achieving an impressive accuracy of 96.78%. This hybrid approach leverages the strengths of ensemble learning methods, enhancing robustness and reliability in classification tasks. On the other hand, the FNI integrates VGG's powerful feature extraction capabilities with the classification strength of Support Vector Machines (SVMs), effectively identifying complex image patterns and achieving an even higher accuracy of 97.41 %. By combining convolutional neural networks (CNNs) for feature extraction with SVMs for classification, FNI capitalizes on the strengths of both techniques, demonstrating superior predictive accuracy compared to traditional methods. These advancements hold significant promise for improving diagnostic accuracy and patient outcomes in AD and other neurological conditions. The methodologies proposed in this study offer a reliable framework for early detection, which is crucial for timely intervention and management of the disease.