{"title":"基于选择性二值编码遗传算法的结构磁共振成像自闭症检测","authors":"Sachnev Vasily, B. S. Mahanand","doi":"10.5626/jcse.2023.17.3.127","DOIUrl":null,"url":null,"abstract":"In this work, an efficient machine learning technique for autism diagnosis using structural magnetic resonance imaging (MRI) is proposed. The proposed technique employs the voxel-based morphometry (VBM) approach to extract a set of 989 relevant features from MRI. These features are used to train an efficient extreme learning machine (ELM) classifier to identify autism spectrum disorder (ASD) and healthy controls. The proposed selective binary coded genetic algorithm (sBCGA) found a subset of significant VBM features. The selected subset of features was used to build a final ELM classifier with maximum overall accuracy. The proposed sBCGA uses a selective sample-balanced crossover designed to improve the classification of ASD and healthy controls. The proposed sBCGA has been extensively tested, and the experimental results clearly indicated better accuracy than existing methods.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Efficient Autism Detection Using Structural Magnetic Resonance Imaging Based on Selective Binary Coded Genetic Algorithm\",\"authors\":\"Sachnev Vasily, B. S. Mahanand\",\"doi\":\"10.5626/jcse.2023.17.3.127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, an efficient machine learning technique for autism diagnosis using structural magnetic resonance imaging (MRI) is proposed. The proposed technique employs the voxel-based morphometry (VBM) approach to extract a set of 989 relevant features from MRI. These features are used to train an efficient extreme learning machine (ELM) classifier to identify autism spectrum disorder (ASD) and healthy controls. The proposed selective binary coded genetic algorithm (sBCGA) found a subset of significant VBM features. The selected subset of features was used to build a final ELM classifier with maximum overall accuracy. The proposed sBCGA uses a selective sample-balanced crossover designed to improve the classification of ASD and healthy controls. The proposed sBCGA has been extensively tested, and the experimental results clearly indicated better accuracy than existing methods.\",\"PeriodicalId\":37773,\"journal\":{\"name\":\"Journal of Computing Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computing Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5626/jcse.2023.17.3.127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computing Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5626/jcse.2023.17.3.127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
An Efficient Autism Detection Using Structural Magnetic Resonance Imaging Based on Selective Binary Coded Genetic Algorithm
In this work, an efficient machine learning technique for autism diagnosis using structural magnetic resonance imaging (MRI) is proposed. The proposed technique employs the voxel-based morphometry (VBM) approach to extract a set of 989 relevant features from MRI. These features are used to train an efficient extreme learning machine (ELM) classifier to identify autism spectrum disorder (ASD) and healthy controls. The proposed selective binary coded genetic algorithm (sBCGA) found a subset of significant VBM features. The selected subset of features was used to build a final ELM classifier with maximum overall accuracy. The proposed sBCGA uses a selective sample-balanced crossover designed to improve the classification of ASD and healthy controls. The proposed sBCGA has been extensively tested, and the experimental results clearly indicated better accuracy than existing methods.
期刊介绍:
Journal of Computing Science and Engineering (JCSE) is a peer-reviewed quarterly journal that publishes high-quality papers on all aspects of computing science and engineering. The primary objective of JCSE is to be an authoritative international forum for delivering both theoretical and innovative applied researches in the field. JCSE publishes original research contributions, surveys, and experimental studies with scientific advances. The scope of JCSE covers all topics related to computing science and engineering, with a special emphasis on the following areas: Embedded Computing, Ubiquitous Computing, Convergence Computing, Green Computing, Smart and Intelligent Computing, Human Computing.