Deep Learning-based framework for Autism functional MRI Image Classification

Xin Yang, S. Sarraf, Ning Zhang
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引用次数: 13

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.
基于深度学习的自闭症功能性MRI图像分类框架
本文的目的是介绍基于深度学习的框架LeNet-5架构,并实现自闭症谱系障碍的功能MRI图像分类实验。我们在NVIDIA深度学习GPU训练系统(DIGITS)下实现我们的实验。采用卷积神经网络(CNN) LeNet-5架构,成功地将自闭症谱系障碍的功能MRI图像与正常对照进行了分类。结果表明,该方法在灵敏度和特异度上均取得了满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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