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An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classification

机译：基于有效的FMRI扫描分类的基于深度学习框架的有效稀疏的AutoEncoders

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Deep Learning (DL) identifies features of medical scans automatically in a way very near to expert doctors and sometimes over beats in treatment procedures. In fact, it increases model generalization as it doesn't focus on low level features and reduces difficulties (eg: overfitting) of training high dimensional data. Therefore, DL becomes a prioritized choice in building most recent Computer-Aided Diagnosis (CAD) systems. From other prospective, Autism Spectrum Disorder (ASD) is a brain disorder characterized by social miscommunication and confusing repetitive behaviours. The accurate diagnosis of ASD through analysing brain scans of patients is considered a research challenge. Some appreciated efforts has been reported in literature, however the problem still needs enhancement and examination of different models. A multi-phase learning algorithm combining supervised and unsupervised approaches is proposed in this paper to classify brain scans of individuals as ASD or controlled patients (TC). First, unsupervised learning is adopted using two sparse autoencoders for feature extraction and refinement of optimal network weights using back-propagation error minimization. Then, third autoencoder act as a supervised classifier. The Autism Brain fMRI (ABIDE-I) dataset is used for evaluation and cross-validation is performed. The proposed model recorded effective and promising results compared to literatures.
机译：深度学习（DL）以非常接近专家医生的方式识别医疗扫描的特征，有时会在治疗程序中的节拍。实际上，它增加了模型泛化，因为它不会关注低级特征，并减少训练高维数据的困难（例如：超容易）。因此，DL成为建立最近的计算机辅助诊断（CAD）系统的优先考虑选择。来自其他前瞻性，自闭症谱系障碍（ASD）是一种以社会误解和令人困惑的重复行为为特征的脑障碍。通过分析患者脑扫描的ASD精确诊断被认为是一个研究挑战。在文献中报告了一些欣赏的努力，但问题仍然需要提高不同模型的增强和检查。在本文中提出了一种组合监督和无监督方法的多相学习算法，以将个体的脑扫描分类为ASD或受控患者（TC）。首先，使用两个稀疏的自动码器采用无监督的学习，用于使用反向传播误差最小化的特征提取和精制最佳网络权重。然后，第三AutoEncoder充当监督分类器。自闭症大脑FMRI（恪验证I）数据集用于评估和交叉验证。与文献相比，拟议的模型记录了有效和有前途的结果。

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