{"title":"基于深度学习的自闭症功能性MRI图像分类框架","authors":"Xin Yang, S. Sarraf, Ning Zhang","doi":"10.54119/jaas.2018.7214","DOIUrl":null,"url":null,"abstract":"The purpose of this paper is to introduce the deep learning-based framework LeNet-5 architecture and implement experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.","PeriodicalId":30423,"journal":{"name":"Journal of the Arkansas Academy of Science","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Deep Learning-based framework for Autism functional MRI Image Classification\",\"authors\":\"Xin Yang, S. Sarraf, Ning Zhang\",\"doi\":\"10.54119/jaas.2018.7214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this paper is to introduce the deep learning-based framework LeNet-5 architecture and implement experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.\",\"PeriodicalId\":30423,\"journal\":{\"name\":\"Journal of the Arkansas Academy of Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Arkansas Academy of Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54119/jaas.2018.7214\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Arkansas Academy of Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54119/jaas.2018.7214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learning-based framework for Autism functional MRI Image Classification
The purpose of this paper is to introduce the deep learning-based framework LeNet-5 architecture and implement experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.