Automated Alzheimer's disease detection from brain magnetic resonance imaging using a smart classifier fusion approach
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Abstract
This aim of this research was to use a novel method for discriminating between Alzheimer's disease from normal controls from brain magnetic resonance imaging. Here, the six diverse connecting rules (mean, product, maximum, minimum, and voting) related to the consolidation of classifiers. The collection of the relevant benchmark data was taken from the Alzheimer's disease neuroimaging initiative (ADNI) data set for the proposal evaluation. The empirical investigations uncovered the four individual classifiers out of thirteen classifiers, viz BayesNet, linear discriminant classifier, quadratic Bayes normal classifier, and kernel support vector machine from numerous machine learning groups, gained the highest recognition percentages of 74.77%, 71.62%, 77.76, and 76.13%, respectively. These four-best performing classifiers were employed for prototyping the classifier fusion model, which displayed a much healthier performance with a shared mean error rate of 0.2123, in contrast to the mean error rate of 0.2493 before ensemble. Our analyses have shown that a smart classifier-based fusion method outperforms the base-classifier method.
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