Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy

As a non-invasive diagnostic tool, Magnetic Resonance Imaging (MRI) has been widely used in the field of brain imaging. The classification of MRI brain image conditions poses challenges both technically and clinically, as MRI is primarily used for soft tissue anatomy and can generate large amounts of detailed information about the brain conditions of a subject. To classify benign and malignant MRI brain images, we propose a new method. Discrete wavelet transform (DWT) is used to extract wavelet coefficients from MRI images. Then, Tsallis entropy with DNA genetic algorithm (DNA-GA) optimization parameters (called DNAGA-TE) was used to obtain entropy characteristics from DWT coefficients. At last, DNA-GA optimized support vector machine (called DNAGA-KSVM) with radial basis function (RBF) kernel, is applied as a classifier. In our experimental procedure, we use two kinds of images to validate the availability and effectiveness of the algorithm. One kind of data is the Simulated Brain Database and another kind of image is real MRI images which downloaded from Harvard Medical School website. Experimental results demonstrate that our method (DNAGA-TE+KSVM) obtained better classification accuracy.

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Machine Learning Classification of Traumatic Brain Injury Patients and Healthy Controls Using Multiple Indices of Diffusion Tensor Imaging

10.21203/rs.3.rs-42800/v1 ◽

2020 ◽

Author(s):

Hiba Abuelgasim Fadlelmoula Abdelrahman ◽

Shiho Ubukata ◽

Keita Ueda ◽

Gaku Fujimoto ◽

Naoya Oishi ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Diffusion Tensor Imaging ◽

Brain Injury ◽

White Matter ◽

Diffusion Tensor ◽

Area Under The Curve ◽

Support Vector ◽

Healthy Controls

Abstract Background: Diffusion tensor imaging (DTI) indices provide quantitative measures of white matter microstructural changes following traumatic brain injury (TBI). However, there is still insufficient evidence for their use as predictive measures. Recently, there has been growing interest in using machine learning (ML) approaches to aid the diagnosis of many neurological and psychiatric illnesses including TBI. The aim of this study is to examine the potential of using multiple DTI indices in conjunction with ML to automate the classification of healthy subjects and patients with TBI across a spectrum of TBI severity.Methods: Participants were adult patients with chronic TBI (n=26) and age and gender-matched healthy controls (n=26). DTI images were obtained from all the participants. Tract-based spatial statistics (TBSS) analysis was applied to the DTI images. Classification models were built using principle component analysis (PCA) and support vector machines (SVM). Receiver operator characteristic (ROC) curve analysis and area under the curve (AUC) were used to assess the classification performance of the different classifiers.Results: The whole-brain white matter TBSS analyses showed significantly decreased FA, as well as increased MD, AD, and RD in TBI patients compared with healthy controls (all p-value < 0.01). The PCA and SVM-based ML classification using combined DTI indices classified TBI patients and healthy controls with the accuracy of 90.5% with an area under the curve (AUC) of 93 +/- 0.09.Conclusion: This study demonstrates the potential of a joint DTI and ML approach for objective classification of TBI patients and healthy controls.

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Classification of Social Anxiety Disorder With Support Vector Machine Analysis Using Neural Correlates of Social Signals of Threat

Frontiers in Psychiatry ◽

10.3389/fpsyt.2020.00144 ◽

2020 ◽

Vol 11 ◽

Author(s):

Mengqi Xing ◽

Jacklynn M. Fitzgerald ◽

Heide Klumpp

Keyword(s):

Support Vector Machine ◽

Anxiety Disorder ◽

Social Anxiety ◽

Social Anxiety Disorder ◽

Neural Correlates ◽

Support Vector ◽

Social Signals ◽

Machine Analysis

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Comparison of four variable selection methods to determine the important variables in predicting the prognosis of traumatic brain injury patients by support vector machine

Journal of Research in Medical Sciences ◽

10.4103/jrms.jrms_89_18 ◽

2019 ◽

Vol 24 (1) ◽

pp. 97 ◽

Cited By ~ 1

Author(s):

Hosseinali Khalili ◽

Saeedeh Pourahmad ◽

Soheila Rasouli-Emadi ◽

Fatemeh Moayyedi

Keyword(s):

Traumatic Brain Injury ◽

Support Vector Machine ◽

Brain Injury ◽

Variable Selection ◽

Support Vector ◽

Selection Methods

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Methods of Support Vector Machine on Classification of Expansive Soils

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.531.562 ◽

2012 ◽

Vol 531 ◽

pp. 562-565 ◽

Cited By ~ 1

Author(s):

Hai Ying Yang ◽

Yun Liu

Keyword(s):

Support Vector Machine ◽

Expansive Soils ◽

Expansive Soil ◽

Liquid Limit ◽

Classification Model ◽

Support Vector ◽

Large Set ◽

Practical Engineering ◽

Machine Analysis

The classification of the grade of shrink and expansion for the expansive soils was the initial and essential work for engineering construction in expansive soil area. Based on the principle of support vector machine analysis, a classification model of expansive was established in this paper, including five indexes reflecting the shrink and expansion of expansive soil, liquid limit, swell-shrink total ratio, plasticity index, water contents and free expansive ratio and functions were obtained through training a large set of expansive samples. It was shown that the classification model of SVM analysis is an effective method performed excellently with high prediction accuracy and could be used in practical engineering.

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