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

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 optimizati...

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Vydáno v:International journal of imaging systems and technology Ročník 30; číslo 1; s. 57 - 74
Hlavní autoři: Bharanidharan, N., Rajaguru, Harikumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.03.2020
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Témata:
Accuracy
Algorithms
Ant colony optimization
Classification
Classifiers
DA‐PSO
Dementia
hybrid swarm intelligence
MRI
OASIS
Optimization
Performance enhancement
Scientific papers
Swarm intelligence
ISSN:0899-9457, 1098-1098
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Shrnutí: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%.
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ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22365