  Deep learning, a cutting-edge machine learning approach, outperforms
traditional machine learning in identifying intricate structures in complex
high-dimensional data, particularly in the domain of healthcare. This study
focuses on classifying Magnetic Resonance Imaging (MRI) data for Alzheimer's
disease (AD) by leveraging deep learning techniques characterized by
state-of-the-art CNNs. Brain imaging techniques such as MRI have enabled the
measurement of pathophysiological brain changes related to Alzheimer's disease.
Alzheimer's disease is the leading cause of dementia in the elderly, and it is
an irreversible brain illness that causes gradual cognitive function disorder.
In this paper, we train some benchmark deep models individually for the
approach of the solution and later use an ensembling approach to combine the
effect of multiple CNNs towards the observation of higher recall and accuracy.
Here, the model's effectiveness is evaluated using various methods, including
stacking, majority voting, and the combination of models with high recall
values. The majority voting performs better than the alternative modelling
approach as the majority voting approach typically reduces the variance in the
predictions. We report a test accuracy of 90% with a precision score of 0.90
and a recall score of 0.89 in our proposed approach. In future, this study can
be extended to incorporate other types of medical data, including signals,
images, and other data. The same or alternative datasets can be used with
additional classifiers, neural networks, and AI techniques to enhance
Alzheimer's detection.
