Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly-based hybrid algorithms

Abstract

Most often clinicians require automated computer-aided MRI classification techniques to substantiate the status of dementia accurately. In this research paper, dragonfly-based features are used to improve the accuracy of well-known swarm intelligence algorithms specifically particle swarm optimization, artificial bee colony, and ant colony optimization in dementia classification. Cross-sectional MRI of 65 non-dementia and 52 dementia subjects were collected from the OASIS database and analyzed. The dementia classification performance of above-mentioned three individual swarm intelligence algorithms is compared with non-swarm intelligence algorithm—Fuzzy C means. A further comparison was made on the improvisation of above-mentioned swarm intelligence algorithms while using dragonfly-based features and Fuzzy C means-based features. Although many swarm intelligence algorithms are reported in the literature, it is ingenious to use dragonfly-based features for improving the performance of individual swarm intelligence algorithms in dementia classification. With proper weight parameters, Dragonfly-particle swarm optimization hybrid classifier yields the highest accuracy of 87.18%, whereas all the above-mentioned individual classifiers yield accuracy of less than 66%.