The objective of this research paper is to categorize the magnetic resonance imaging (MRI) images as demented (DEM) or nondemented (ND) using improved chicken swarm optimization technique (ICSO). In literature, CSO technique is widely used to solve numerical optimization and feature selection problem. Using this optimization technique for medical image classification problem will be a pioneering idea. If this technique is directly used to classify the medical images, it provides poor results. Hence, appropriate enhancements are made on the original algorithm using a novel controlled randomness optimization algorithm and control parameter tuning. Cross‐over and Rooster selection methods are also implemented in cascaded manner for further performance improvization. All the experiments are made for two cases: with and without statistical features. The brain MRI images of 65 ND and 52 DEM subjects obtained from the Open Access Series of Imaging Studies website are used in this analysis. The ICSO without statistical features provides the highest accuracy of 86.32%, whereas the original chicken swarm optimization technique provides the accuracy of 52.13% and 52.99% with and without statistical features, respectively.

中文翻译：

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改进的鸡群优化算法，使用新型受控随机性优化算法对痴呆症MRI图像进行分类

本研究的目的是使用改进的鸡群优化技术（ICSO）将磁共振成像（MRI）图像分类为痴呆（DEM）或非痴呆（ND）。在文献中，CSO技术被广泛用于解决数值优化和特征选择问题。将这种优化技术用于医学图像分类问题将是一个开创性的想法。如果直接使用此技术对医学图像进行分类，则结果较差。因此，使用新颖的受控随机性优化算法和控制参数调整对原始算法进行了适当的增强。交叉和公鸡选择方法也以级联的方式实施，以进一步改善性能。所有实验都是针对两种情况进行的：具有和不具有统计特征。从成像研究的开放获取系列网站获得的65名ND和52名DEM受试者的大脑MRI图像用于此分析。不具有统计功能的ICSO的最高准确度为86.32％，而原始的鸡群优化技术在具有和不具有统计功能的情况下的准确度分别为52.13％和52.99％。