Brain Network Analysis and Classification Based on Convolutional Neural Network

Background: Convolution neural networks (CNN) is increasingly used in computer science and finds more and more applications in different fields. However, analyzing brain network with CNN is not trivial, due to the non-Euclidean characteristics of brain network built by graph theory.Method: To address this problem, we used a famous algorithm “word2vec” from the field of natural language processing (NLP), to represent the vertexes of graph in the node embedding space, and transform the brain network into images, which can bridge the gap between brain network and CNN. Using this model, we analyze and classify the brain network from Magnetoencephalography (MEG) data into two categories: normal controls and patients with migraine.Results: In the experiments, we applied our method on the clinical MEG dataset, and got the mean classification accuracy rate 81.25%.Conclusions: These results indicate that our method can feasibly analyze and classify the brain network, and all the abundant resources of CNN can be used on the analysis of brain network

Self-attention based high order sequence feature reconstruction of dynamic functional connectivity networks with rs-fMRI for brain disease classification

Dynamic functional connectivity networks (dFCN) based on rs-fMRI have demonstrated tremendous potential for brain function analysis and brain disease classification. Recently, studies have applied deep learning techniques (i.e., convolutional neural network, CNN) to dFCN classification, and achieved better performance than the traditional machine learning methods. Nevertheless, previous deep learning methods usually perform successive convolutional operations on the input dFCNs to obtain high-order brain network aggregation features, extracting them from each sliding window using a series split, which may neglect non-linear correlations among different regions and the sequentiality of information. Thus, important high-order sequence information of dFCNs, which could further improve the classification performance, is ignored in these studies. Nowadays, inspired by the great success of Transformer in natural language processing and computer vision, some latest work has also emerged on the application of Transformer for brain disease diagnosis based on rs-fMRI data. Although Transformer is capable of capturing non-linear correlations, it lacks accounting for capturing local spatial feature patterns and modelling the temporal dimension due to parallel computing, even equipped with a positional encoding technique. To address these issues, we propose a self-attention (SA) based convolutional recurrent network (SA-CRN) learning framework for brain disease classification with rs-fMRI data. The experimental results on a public dataset (i.e., ADNI) demonstrate the effectiveness of our proposed SA-CRN method

Convolutional Gated Recurrent Neural Network Based Automatic Detection and Classification of Brain Tumor using Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) might be a problematic assignment for tumor fluctuation and complexity because of brain image classification. This work presents the brain tumor classification system using Convolutional Gated Recurrent Neural Network (CGRNN) algorithm based on MRI images. The proposed tumor recognition framework comprises of four stages, to be specific preprocessing, feature extraction, segmentation and classification. Extraction of identified tumor framework features was accomplished utilizing Gray Level Co-occurrence Matrix (GLCM) strategy. At long last, the Convolutional Gated Recurrent Neural Network Classifier has been created to perceive various kinds of brain disease. The proposed framework can be effective in grouping these models and reacting to any variation from the abnormality. The entire framework is isolated into different types of phases: the Learning/Training Phase and the Recognition/Test Phase. A CGRNN classifier under the scholarly ideal separation measurements is utilized to decide the chance of every pixel having a place with the foreground (tumor) and the background. MATLAB software is used in the development of the simulation of the proposed system. The suggested method's simulation results show that the analysis of brain tumours is stable. It shows that the proposed brain tumor classifications are superior to those from brain MRIs than existing brain tumor classifications. The overall accuracy of the proposed system is 98.45%

Convolutional Neural Network for Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

Brain-computer interface (BCI) is a communication system that translates the brain signal directly to a computer or external devices. It is a promising solution for the patients with neurological disorders as the system is able to restore the movement ability. Various neuroimaging modalities have been utilized for brain signal acquisition, however, functional near-infrared spectroscopy (fNIRS) provides many advantages over other modalities. Hence, it has gained attention for implementing in BCI system. For developing BCI system, the appropriate machine learning algorithm and discriminating features from the hemodynamic response signal are desired, as the previous studies have reported the performance enhancement of fNIRS-based BCI in terms of classification accuracy by focusing on the classifier as well as signal features. The aim of this thesis is to improve the classification accuracy in fNIRS-based BCI by classifying and extracting feature automatically. The convolutional neural network (CNN) was applied owing to the automatic feature extractor and classifier instead of manual feature extraction in the conventional methods. In the experiment, four healthy subjects were measured the hemodynamic response signal evoked by performing tasks including rest, right and left hand motor executions. The conventional methods of fNIRS-based BCI using signal mean, slope, peak, variance, skewness, and kurtosis as the features, and support vector machine (SVM) and artificial neural network (ANN) as the classification methods were compared with CNN-based method. The results show the improvement of classification accuracy of CNN-based method over SVM-based and ANN-based method 6.92% and 3.75%, respectively. The main contributions of this thesis are (1) the promising feature extraction and classification method for fNIRS-based BCI using CNN and (2) the analysis of the feature extracted by conventional methods and convolutional filter of the CNN. ⓒ 2017 DGISTprohibitionI. INTRODUCTION 1-- 1. Motivation 1-- 2. Objective 2-- II. BACKGROUND AND RELATEDWORK 4-- 1. Functional Near-Infrared Spectroscopy (fNIRS) 4-- 2. fNIRS-based BCI 5-- 3. Feature Extraction and Classification 6-- 3.1 Feature Extraction 6-- 3.2 Support Vector Machine (SVM) 7-- 3.3 Artificial Neural Network (ANN) 7-- 3.4 Convolutional Neural Network (CNN) 9-- 4. Evaluation 11-- III. METHOD 12-- 1. Participants 12-- 2. Data Acquisition 12-- 3. Experimental Procedure 12-- 4. Preprocessing 13-- 4.1 Concentration Changes of Hemoglobin 13-- 4.2 Filtering 14-- 5. Feature Extraction and Classification 16-- 5.1 Conventional Method 16-- 5.2 Proposed Structures of CNN 17-- 6. Feature Visualization 20-- IV. RESULTS AND DISCUSSIONS 23-- 1. Measured Hemodynamic Responses 23-- 2. Classification Accuracy 24-- 3. Feature Visualization 26-- 4. Future Work 28-- V. CONCLUSION 30-- References 31-- Acknowledgments 38-- Curriculum Vitae 39MasterdCollectio