基于和谐搜索优化技术的痴呆分类

B. N, H. Rajaguru
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引用次数: 7

摘要

软计算技术可用于痴呆的自动分类,帮助临床医生对痴呆进行诊断。本研究采用和声搜索优化技术,通过MRI图像对痴呆进行分类。在文献中,Harmony Search算法被广泛用于优化问题、特征选择和训练神经网络。但是使用Harmony Search对医学图像进行分类是很有创意的。OASIS横截面数据集包含30名非痴呆和30名痴呆患者的MRI脑图像,用于该分析。在选取了考虑音调调节率和和声记忆的最优值后,该方法在痴呆分类中的良率检测率为94.73%,而粒子群算法和最优权重人工蜂群算法的良率检测率分别为64.15%和62.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Dementia Using Harmony Search Optimization Technique
Soft computing techniques can be used in automated classification of dementia, to help the clinician in dementia diagnosis. This research paper uses Harmony Search optimization technique to classify dementia through MRI images. In literature, Harmony Search algorithm is used extensively for optimization problem, feature selection and training Neural Networks. But using Harmony Search for classification of medical images is ingenious. OASIS cross sectional dataset containing MRI brain images of 30 non-dementia and 30 dementia patients are used in this analysis. After the selection of optimum values for Harmony Memory Considering Rate and Pitch Adjusting Rate, this technique yields Goodness Detection Ratio of 94.73% while Particle Swarm optimization and Artificial Bee Colony with optimum weights yields only 64.15% and 62.7% in dementia classification respectively.
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