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%.

中文翻译：

---

基于蜻蜓混合算法的群体智能技术在痴呆分类中的性能增强

大多数情况下，临床医生需要自动计算机辅助 MRI 分类技术来准确证实痴呆症的状态。在这篇研究论文中，基于蜻蜓的特征被用来提高著名的群体智能算法的准确性，特别是粒子群优化、人工蜂群和痴呆分类中的蚁群优化。从 OASIS 数据库中收集并分析了 65 名非痴呆和 52 名痴呆受试者的横断面 MRI。将上述三种个体群体智能算法的痴呆分类性能与非群体智能算法——Fuzzy C均值进行比较。在使用基于蜻蜓的特征和基于模糊 C 均值的特征时，对上述群体智能算法的即兴进行了进一步的比较。尽管文献中报道了许多群体智能算法，但巧妙地使用基于蜻蜓的特征来提高个体群体智能算法在痴呆分类中的性能。在适当的权重参数下，蜻蜓粒子群优化混合分类器的准确率最高，为 87.18%，而上述所有单个分类器的准确率均低于 66%。