scholarly journals Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiaocheng Zhou ◽  
Qingmin Lin ◽  
Yuanyuan Gui ◽  
Zixin Wang ◽  
Manhua Liu ◽  
...  
Keyword(s):  
Multiple Kernel Learning ◽  
Early Adolescent ◽  
Brain Diseases ◽  
Healthy Children ◽  
Adhd Diagnosis ◽  
Hyperactivity Disorder ◽  
Multiple Kernel ◽  
Multimodal Features ◽  
Kernel Level ◽  
And Diffusion

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9–10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis.

scholarly journals Multiple Modal Features and Multiple Kernel Learning for Human Daily Activity Recognition

2018 ◽  
Vol 21 (2) ◽  
pp. 52-63 ◽  
Author(s):  
Viet Hoai Vo ◽  
Hoang Minh Pham
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Daily Activity ◽  
Intelligent Systems ◽  
Learning Algorithm ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Multiple Features ◽  
Multiple Kernel ◽  
Multimodal Features

Introduction: Recognizing human activity in a daily environment has attracted much research in computer vision and recognition in recent years. It is a difficult and challenging topic not only inasmuch as the variations of background clutter, occlusion or intra-class variation in image sequences but also inasmuch as complex patterns of activity are created by interactions among people-people or people-objects. In addition, it also is very valuable for many practical applications, such as smart home, gaming, health care, human-computer interaction and robotics. Now, we are living in the beginning age of the industrial revolution 4.0 where intelligent systems have become the most important subject, as reflected in the research and industrial communities. There has been emerging advances in 3D cameras, such as Microsoft's Kinect and Intel's RealSense, which can capture RGB, depth and skeleton in real time. This creates a new opportunity to increase the capabilities of recognizing the human activity in the daily environment. In this research, we propose a novel approach of daily activity recognition and hypothesize that the performance of the system can be promoted by combining multimodal features. Methods: We extract spatial-temporal feature for the human body with representation of parts based on skeleton data from RGB-D data. Then, we combine multiple features from the two sources to yield the robust features for activity representation. Finally, we use the Multiple Kernel Learning algorithm to fuse multiple features to identify the activity label for each video. To show generalizability, the proposed framework has been tested on two challenging datasets by cross-validation scheme. Results: The experimental results show a good outcome on both CAD120 and MSR-Daily Activity 3D datasets with 94.16% and 95.31% in accuracy, respectively. Conclusion: These results prove our proposed methods are effective and feasible for activity recognition system in the daily environment.  

scholarly journals Multiscale Adjacent Superpixel-Based Extended Multi-Attribute Profiles Embedded Multiple Kernel Learning Method for Hyperspectral Classification

2020 ◽  
Vol 13 (1) ◽  
pp. 50
Author(s):  
Lei Pan ◽  
Chengxun He ◽  
Yang Xiang ◽  
Le Sun
Keyword(s):  
Principal Components ◽  
Hyperspectral Image ◽  
Learning Algorithm ◽  
Multiple Kernel Learning ◽  
Principal Component ◽  
Kernel Learning ◽  
Multiple Kernel ◽  
Multimodal Features ◽  
Optimal Kernel ◽  
Hyperspectral Classification

In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in both spatial and spectral dimensions for hyperspectral image (HSI) classification. First, the original HSI is reduced to three principal components in the spectral domain using principal component analysis (PCA). Then, a fast and efficient segmentation algorithm named simple linear iterative clustering is utilized to segment the principal components into a certain number of superpixels. By setting different numbers of superpixels, a set of multiscale homogenous regional features is extracted. Based on those extracted superpixels and their first-order adjacent superpixels, EMAPs with multimodal features are extracted and embedded into the multiple kernel framework to generate different spatial and spectral kernels. Finally, a PCA-based kernel learning algorithm is used to learn an optimal kernel that contains multiscale and multimodal information. The experimental results on two well-known datasets validate the effectiveness and efficiency of the proposed method compared with several state-of-the-art HSI classifiers.

Robust Subspace Clustering Based on Automatic Weighted Multiple Kernel Learning

2021 ◽  
Author(s):  
Guo ◽  
Xiaoqian Zhang ◽  
Zhigui Liu ◽  
Xuqian Xue ◽  
Qian Wang ◽  
...  
Keyword(s):  
Multiple Kernel Learning ◽  
Subspace Clustering ◽  
Kernel Learning ◽  
Multiple Kernel

scholarly journals MicroRNA expression classification for pediatric multiple sclerosis identification

2021 ◽  
Author(s):  
Gabriella Casalino ◽  
Giovanna Castellano ◽  
Arianna Consiglio ◽  
Nicoletta Nuzziello ◽  
Gennaro Vessio
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Healthy Children ◽  
Multifactorial Diseases ◽  
Hyperactivity Disorder ◽  
Pediatric Multiple Sclerosis ◽  
Mirna Expression Profiles ◽  
Selection Algorithms ◽  
Expression Classification

Abstract MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.

scholarly journals DRD4 Gene Polymorphisms as a Risk Factor for Children with Attention Deficit Hyperactivity Disorder in Iranian Population

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Seyed Mahmoud Tabatabaei ◽  
Shahrokh Amiri ◽  
Sara Faghfouri ◽  
Seyed Gholamreza Noorazar ◽  
Shahin AbdollahiFakhim ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Tandem Repeats ◽  
Receptor Gene ◽  
Variable Number ◽  
Healthy Children ◽  
Drd4 Gene ◽  
Hyperactivity Disorder ◽  
Significant Difference ◽  
Northwest Of Iran

Background and Objective. Dopamine dysfunction is known to be associated with attention deficit hyperactivity disorder (ADHD). Dopamine D4 receptor gene (DRD4) is one of the important genes in this pathway. This study intended to investigate the variable number of tandem repeats (VNTR) in exon 3 of the DRD4 gene in Iranian children and adolescents. Materials and Methods. In this study, 130 children with ADHD, aged 6–14 years, and 130 healthy children, within the same age range, were enrolled. All children were selected from northwest of Iran which have Caucasian ethnic background and are of a Turkic ethnic group. VNTR polymorphisms of the DRD4 gene were evaluated by PCR using exon 3-specific primers followed by agarose gel electrophoresis. Findings. The Hardy-Weinberg principle and Chi-square test showed a significant difference in 4-repetition (4R) alleles between the ADHD (76.2%) and control (53.8%) groups (p=0.004; X2=17.39; df=5). The least percentage of repetition alleles in both groups was 2R. Conclusion. There is a significant correlation between the 4R alleles of DRD4 and ADHD in the northwest of Iran.

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