scholarly journals Classification of MRI Brain Images Using DNA Genetic Algorithms Optimized Tsallis Entropy and Support Vector Machine

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 964 ◽  
Author(s):  
Wenke Zang ◽  
Zehua Wang ◽  
Dong Jiang ◽  
Xiyu Liu ◽  
Zhenni Jiang
Keyword(s):  
Support Vector Machine ◽  
Tsallis Entropy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Brain Images ◽  
Mri Brain ◽  
Rbf Kernel ◽  
Optimization Parameters ◽  
Mri Brain Images

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.

Recognition and Classification of Human Activity by Posture Sensing and Machine Learning

2012 ◽  
Vol 468-471 ◽  
pp. 2916-2919
Author(s):  
Fan Yang ◽  
Yu Chuan Wu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Human Activity ◽  
Learning Algorithm ◽  
Optimal Parameter ◽  
Support Vector ◽  
Rbf Kernel ◽  
Human Activity Detection ◽  
Near Future

This paper describes how to use a posture sensor to validate human daily activity and by machine learning algorithm - Support Vector Machine (SVM) an outstanding model is built. The optimal parameter σ and c of RBF kernel SVM were obtained by searching automatically. Those kinematic data was carried out through three major steps: wavelet transformation, Principle Component Analysis (PCA) -based dimensionality reduction and k-fold cross-validation, followed by implementing a best classifier to distinguish 6 difference actions. As an activity classifier, the SVM (Support Vector Machine) algorithm is used, and we have achieved over 94.5% of mean accuracy in detecting differential actions. It shows that the verification approach based on the recognition of human activity detection is valuable and will be further explored in the near future.

A Novel Algorithm for Detection and Classification of Brain Tumors

2015 ◽  
Vol 14 (01) ◽  
pp. 1550006
Author(s):  
M. C. Jobin Christ ◽  
X. Z. Gao ◽  
Kai Zenger
Keyword(s):  
Brain Tumors ◽  
Fuzzy Inference ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Mri Brain ◽  
Hierarchical Topology ◽  
Inference Systems ◽  
Mri Brain Images

Segmentation of an image is the partition or separation of the image into disjoint regions of related features. In clinical practice, magnetic resonance imaging (MRI) is used to differentiate pathologic tissues from normal tissues, especially for brain tumors. The main objective of this paper is to develop a system that can follow a medical technician way of work, considering his experience and knowledge. In this paper, a step by step methodology for the automatic MRI brain tumor segmentation and classification is presented. Initially acquired MRI brain images are preprocessed by the Gaussian filter. After preprocessing, initial segmentation is done by hierarchical topology preserving map (HTPM). From the resultant images, the features are extracted using gray level co-occurrence matrix (GLCM) method, and the same are given as inputs to adaptive neuro fuzzy inference systems (ANFIS) for final segmentation and the classification of brain images into normal or abnormal. In case of abnormal, the MRI brain images are classified as benign subject (tumor without cancerous tissues) or malignant subject (tumor with cancerous tissues). Based on the analysis, it has been discovered that the overall accuracy of classification of our method is above 94%, and F1-score is about 1. The simulation results also show that the proposed approach is a valuable diagnosing technique for the physicians and radiologists to detect the brain tumors.

scholarly journals Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

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