A Deep Ensemble Learning Approach for Automatic AD Detection

Introduction: Early detection of Alzheimer's disease (AD) is crucial due to its rising prevalence and the economic burdens it imposes on individuals and society. This study aimed to propose a technique for the early detection of AD using MRI scans. Method: The methodology involved collecting data, preparing the data, creating both single and combined models, assessing with ADNI data, and confirming with additional datasets. The approach was chosen by comparing various scenarios. The top six individual ConvNet-based classifiers were combined to form the ensemble model. The evaluation showed high accuracy rates across various classification groups. Validation of additional data showed impressive accuracy, exceeding results from numerous previous studies and aligning with others. Results: Although ensemble methods outperformed individual models, there were no notable distinctions among different ensemble approaches. The ensemble model was constructed using the top six individual ConvNet-based classifiers in deep learning (DL), achieving high accuracy rates across various classification categories: 98.66% for Normal control | AD, 96.56% for Normal control | Early MCI, 94.41% Conclusion: Early MCI/Late MCI, 99.96% for Late MCI | AD, 94.19% for four-way classification, and 94.93% for three-way classification. Validation results underscored the limited effectiveness of individual models in practical settings, contrasting with the promising outcomes of the ensemble method.