Classification of Alzheimer's disease using advanced deep learning and ensemble techniques

Alzheimer's disease (AD), a principal contributor to dementia, poses a critical challenge within the domain of neurology, particularly in achieving precise diagnoses and prognoses. Traditional techniques, including basic deep learning and machine learning methods, often fall short in terms of classification accuracy and robustness. This study capitalizes on the capabilities of advanced deep learning via the application of ensemble methodology to refine the accuracy of image-based AD classification. Focusing on Deep Convolutional Neural Networks (DCNNs) with the help of the Mish and ReLU activation functions, this research explores the implementation of models from the Visual Geometry Group (VGG) and experiments with sophisticated architectures such as ResNet 50V2 and ResNet 101V2 along with additional convolutional layers. The introduced ensemble model, which employs ResNet101V2, VGG19, and a customized CNN, uses soft voting with judiciously assigned weights to maximize classification efficacy and achieves an accuracy of 95.125%. The validation of our findings across various metrics, including precision, recall, and AUC, illustrates the significant impact of state-of-the-art deep learning architectures and ensemble methods in the accurate classification of AD stages. The implications of this research contribute markedly to the advancement of AD diagnostic and prognostic practices, signifying a considerable progression in the realms of medical imaging and neurology.