A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation

Jiang, Yizhang; Zhao, Kun; Xia, Kaijian; Xue, Jing; Zhou, Liming; Ding, Yang; Qian, Pengjiang

doi:10.1007/s10916-019-1245-1

Cited by 100 publications

(66 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, computer-aided diagnosis technology based on machine learning has been widely used in medical image analysis in recent years [5][6][7][8][9][10][11][12][13][14]. Since the algorithm based on machine learning can train model parameters through various features of medical images and use the trained model to predict the extracted features, it can well solve the classification, regression, and aggregation in medical images.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

et al. 2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

show abstract

Section: Introductionmentioning

confidence: 99%

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

et al. 2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…It often occurs once a semester and is not rigorous. In summary, this research proposes an automatic anxiety recognition method based on deep features and machine learning [19][20][21][22][23][24][25][26][27]. The main work of this paper is summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features

Meng

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

College students are the most active, most sensitive, and most prone group with respect to various psychological problems in contemporary society. In recent years, with the intensification of social competition, including various pressures such as studies, examinations, economic loss, emotional loss, and employment, the incidence of anxiety, depression and suicide rates has increased. To effectively pay attention to the psychological development of college students and to strengthen mental health education, this research proposes a method to automatically identify the anxiety of college students using a Takagi-Sugeno-Kang (TSK) fuzzy system and deep features. First, preprocess the collected EEG of college students. Secondly, use convolutional neural network (CNN) to extract deep features from the input data. Finally, TSK fuzzy system is used to classify features to obtain the final recognition result. Through experiments on standard data sets and self-made data sets, the experimental results verify the superiority of the anxiety identification method used in this study. The experimental results further demonstrate that the depth features have richer information than traditional features. The noise immunity of TSK fuzzy system makes it show good classification performance and generalization. The recognition results can quickly locate students with anxiety disorders and narrow the scope of investigation for students with psychological problems. The automatic recognition of college students' anxiety can improve the efficiency of schools and teachers in investigating students' psychological problems. This research has very good practical application value.

show abstract

“…Entropy, as a parameter representing the complexity and randomness of the signal, is used as a feature of epilepsy EEG signals to distinguish it from EEG signals of different periods [9,10]. With the development of pattern recognition and machine learning research, various learning algorithms [11][12][13][14][15][16][17][18][19][20] are also widely used in automatic detection of epilepsy. In recent years, with the development of deep learning, some deep neural networks have also been used in the classification of epilepsy EEG signals [21,22].…”

Section: Introductionmentioning

confidence: 99%

An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features

Zhan

2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals. In order to improve the effect of automatic detection, this study proposes a new classification method based on unsupervised multiview clustering results. In addition, considering the high-dimensional characteristics of the original data samples, a deep convolutional neural network (DCNN) is introduced to extract the sample features to obtain deep features. The deep feature reduces the sample dimension and increases the sample separability. The main steps of our proposed novel EEG detection method contain the following three steps: first, a multiview FCM clustering algorithm is introduced, and the training samples are used to train the center and weight of each view. Then, the class center and weight of each view obtained by training are used to calculate the view-weighted membership value of the new prediction sample. Finally, the classification label of the new prediction sample is obtained. Experimental results show that the proposed method can effectively detect seizures.

show abstract

A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation

Cited by 100 publications

References 22 publications

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features

An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features

Contact Info

Product

Resources

About