5 research outputs found
Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls
Convolutional Neural Network (CNN) has been successfully applied on
classification of both natural images and medical images but not yet been
applied to differentiating patients with schizophrenia from healthy controls.
Given the subtle, mixed, and sparsely distributed brain atrophy patterns of
schizophrenia, the capability of automatic feature learning makes CNN a
powerful tool for classifying schizophrenia from controls as it removes the
subjectivity in selecting relevant spatial features. To examine the feasibility
of applying CNN to classification of schizophrenia and controls based on
structural Magnetic Resonance Imaging (MRI), we built 3D CNN models with
different architectures and compared their performance with a handcrafted
feature-based machine learning approach. Support vector machine (SVM) was used
as classifier and Voxel-based Morphometry (VBM) was used as feature for
handcrafted feature-based machine learning. 3D CNN models with sequential
architecture, inception module and residual module were trained from scratch.
CNN models achieved higher cross-validation accuracy than handcrafted
feature-based machine learning. Moreover, testing on an independent dataset, 3D
CNN models greatly outperformed handcrafted feature-based machine learning.
This study underscored the potential of CNN for identifying patients with
schizophrenia using 3D brain MR images and paved the way for imaging-based
individual-level diagnosis and prognosis in psychiatric disorders.Comment: 4 PAGE
Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier
Structural alterations have been thoroughly investigated in the brain during
the early onset of schizophrenia (SCZ) with the development of neuroimaging
methods. The objective of the paper is an efficient classification of SCZ in 2
different classes: Cognitive Normal (CN), and SCZ using magnetic resonance
imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural
network (CNN) based framework for SCZ diagnosis using MRI images. In the
proposed model, lightweight 3D CNN is used to extract both spatial and spectral
features simultaneously from 3D volume MRI scans, and classification is done
using an ensemble bagging classifier. Ensemble bagging classifier contributes
to preventing overfitting, reduces variance, and improves the model's accuracy.
The proposed algorithm is tested on datasets taken from three benchmark
databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. These
datasets have undergone preprocessing steps to register all the MRI images to
the standard template and reduce the artifacts. The model achieves the highest
accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall
94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current
state-of-the-art techniques. The performance metrics evidenced the use of this
model to assist the clinicians for automatic accurate diagnosis of SCZ
Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition
Deep learning has been successfully applied to recognizing both natural
images and medical images. However, there remains a gap in recognizing 3D
neuroimaging data, especially for psychiatric diseases such as schizophrenia
and depression that have no visible alteration in specific slices. In this
study, we propose to process the 3D data by a 2+1D framework so that we can
exploit the powerful deep 2D Convolutional Neural Network (CNN) networks
pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition.
Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics (grey
matter, white matter, and cerebrospinal fluid) are decomposed to 2D slices
according to neighboring voxel positions and inputted to 2D CNN models
pre-trained on the ImageNet to extract feature maps from three views (axial,
coronal, and sagittal). Global pooling is applied to remove redundant
information as the activation patterns are sparsely distributed over feature
maps. Channel-wise and slice-wise convolutions are proposed to aggregate the
contextual information in the third view dimension unprocessed by the 2D CNN
model. Multi-metric and multi-view information are fused for final prediction.
Our approach outperforms handcrafted feature-based machine learning, deep
feature approach with a support vector machine (SVM) classifier and 3D CNN
models trained from scratch with better cross-validation results on publicly
available Northwestern University Schizophrenia Dataset and the results are
replicated on another independent dataset
Multi-Kernel Capsule Network for Schizophrenia Identification
Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multi-kernel capsule structure with consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification
Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy