Graph-Kernel Based Structured Feature Selection for Brain Disease Classification Using Functional Connectivity Networks

Wang, Mi; Bian, Weiwei; Ding, Xintao; Zhou, Wen; Wang, Zhengdong; Liu, Mingxia

doi:10.1109/access.2019.2903332

Cited by 28 publications

(16 citation statements)

References 60 publications

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“…The study uses probability concept for constructing structures and then it extracts the probable ridges of the brain. Similar direction of work was also carried out by Wang et al [19]. The technique constructs a connected network formulated from the kernel technique of graph followed by computing the correlation for such networks.…”

Section: Introductionmentioning

confidence: 87%

An optimized approach for extensive segmentation and classification of brain MRI

Harish¹,

Ahammed²

2020

IJECE

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With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.

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Section: Introductionmentioning

confidence: 87%

An optimized approach for extensive segmentation and classification of brain MRI

Harish¹,

Ahammed²

2020

IJECE

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“…MDL also measures quality with the below equation. The etiquette equation is as follows [2]. (kon95 in the thesis)…”

Section: Minimum Description Length (Mdl)mentioning

confidence: 99%

“…In Ref. [2], Wang evaluates an approach for structural discrimination of networks and also to estimate discriminate features in brain disease classification, graph kernel-based structured feature selection (gk-SFS). Meanwhile, to improve performance, l 1 -norm based sparsity regularizer deployed in [2].…”

Section: Introductionmentioning

confidence: 99%

“…[2], Wang evaluates an approach for structural discrimination of networks and also to estimate discriminate features in brain disease classification, graph kernel-based structured feature selection (gk-SFS). Meanwhile, to improve performance, l 1 -norm based sparsity regularizer deployed in [2]. Performance achieves by comparing the accuracy of proposed one with other existing approaches, as well as considers eliminating noisy or redundant information in [2].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, to improve performance, l 1 -norm based sparsity regularizer deployed in [2]. Performance achieves by comparing the accuracy of proposed one with other existing approaches, as well as considers eliminating noisy or redundant information in [2]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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Extraction with Map-Reduce Framework and Correlation-based Feature Selection in Lung Cancer Towards Big Data

Seenivasagam¹

2020

IJST

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Background/objectives: To extract nucleus and cytoplasm that intend to optimize features in high-dimensional images such as all types of raw sputum cells. To calculate following features efficiently: Area, Perimeter, Intensity, NC Ratio, and Circularity. Methods/Statistical analysis: To take results in proposed stride, we introduced map-reduce framework for separating similar cells from sputum cell images that have been collected from Microscope lab images with intended magnification and staining. To avoid model learn from irrelevant features, feature selection methods with correlation-based feature selection contributes appropriate features that are then fed for classification. Features here converted to vectors for the estimation of symmetric uncertainty, correlationbased approach. Findings: Performance evaluation metrics checks into the contribution to measure it's out coming performance. Even though lot of works relied on feature extraction, our work combines feature extraction with map-reduce framework which improves accuracy for classification. Our proposed method makes extraction of nucleus and cytoplasm easier than other methods. Optimized performance assured in proposed feature selection. Novelty/ applications: Eventual accuracy for every feature in proposed stride improves than other existing works. In addition, ROC curves proves higher true positive rate even in increased datasets. Another significant innovation in our work is map-reduce framework applies in images to sort cells with respect to staining.

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High‐Order line graphs of fMRI data in major depressive disorder

Guo,

Huang,

Wang

et al. 2024

Medical Physics

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BackgroundResting‐state functional magnetic resonance imaging (rs‐fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high‐order topology of hypergraphs. In addition, the study of multi‐scale and multi‐layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression.PurposeThis work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high‐order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance.MethodsBased on rs‐fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high‐order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi‐kernel learning. The Kolmogorov–Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave‐one‐out cross‐validation method.ResultsThe high‐order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy = 92.42%, sensitivity = 92.86%, specificity = 92.11%, precision = 89.66%, F1 = 91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression.ConclusionsThis work validated the classification model based on the high‐order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

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Graph-Kernel Based Structured Feature Selection for Brain Disease Classification Using Functional Connectivity Networks

Cited by 28 publications

References 60 publications

An optimized approach for extensive segmentation and classification of brain MRI

An optimized approach for extensive segmentation and classification of brain MRI

Extraction with Map-Reduce Framework and Correlation-based Feature Selection in Lung Cancer Towards Big Data

High‐Order line graphs of fMRI data in major depressive disorder

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