Abstract
Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.
Highlights
Medical imaging refers to several different technologies that are used to provide visual representations of the interior of the human body in order to aid the radiologists and clinicians to detect, diagnose, or treat diseases early and more efficiently (Brody, 2013)
We introduce the fundamental concept of basic deep learning models in the literature, which have been wildly applied to medical image analysis, especially human brain disorder diagnosis
Compared to the stacked auto-encoders (SAE), Deep Belief Network (DBN), and Deep Boltzmann Machine (DBM), utilizing the inputs in vector form which inevitably destroys the structural information in images, the convolutional neural network (CNN) is designed to better retain and utilize the structural information among neighboring pixels or voxels and to required minimal preprocessing by directly taking two-dimensional (2D) or three-dimensional (3D) images as inputs (LeCun et al, 1998)
Summary
Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer’s disease, Parkinson’s disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively.
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