Predicting Alzheimer's Disease Onset: An Efficient Weighted Probability-Based Deep Ensemble Learning Method (WPBDELM) Using MRI Images
Keywords:
Dementia, Alzheimer’s disease, Ensemble learning, Deep learning, Mild cognitive impairment, Deep learning, Convolutional neural networks, Transfer learning, Magnetic resonance imaging, Transfer learningAbstract
Recent advancements in deep learning have enabled innovative approaches to analyze large datasets, particularly within the healthcare sector. Convolutional neural networks (CNNs) are becoming essential tools for classifying medical imaging data, with increasing attention on their application in neuroscience for Alzheimer's disease (AD) classification. Given that Alzheimer’s is the most frequent cause of dementia in the aging population, detection at its early stages is critical. Early diagnosis depends on noninvasive imaging procedures, namely positron emission tomography (PET) and magnetic resonance imaging (MRI). There is much promise for enhancing early and precise AD detection through the analysis of several imaging modalities using CNNs. Furthermore, by combining several models, ensemble learning (EL) can greatly improve the performance of machine learning systems. This work presents an ensemble method for early AD diagnosis using MRI images using deep learning. The proposed method combines six prominent CNNs into an ensemble model, selected through a novel technique called the weighted probability-based deep ensemble learning method (WPBDELM). The study involved collecting and preprocessing data, developing individual and ensemble models, and evaluating them using ADNI data. The evaluation demonstrated high accuracy rates: 98.57% (NC/AD), 98.37% (NC/EMCI), 98.22% (EMCI/LMCI), 99.83% (LMCI/AD), 98.72% (three-way classification), and 98.78% (four-way classification). These results not only exceeded those of most reviewed studies but also were on par with the best-performing methods. Although individual models were outperformed by ensemble methods, there were no discernible differences between the different ensemble techniques. The evaluation outcomes demonstrated that although individual models performed less well in practice, the ensemble approach produced reliable and encouraging results.
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