Automatic Classification of Brain Diseases in MR Images Using Genetic Algorithm and Support Vector Machine

A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.

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A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine

10.1117/12.813256 ◽

2008 ◽

Cited By ~ 36

Author(s):

Li Zhuo ◽

Jing Zheng ◽

Xia Li ◽

Fang Wang ◽

Bin Ai ◽

...

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Hyperspectral Images ◽

Support Vector ◽

Wrapper Feature Selection

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

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2176104 ◽

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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Automatic Prostate Lesions Detection on MR Images Based on the Ising Model

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2019.8019 ◽

2019 ◽

Vol 16 (2) ◽

pp. 341-350

Author(s):

Artur Bernardo Silva Reis ◽

Aristófanes Corrêa Silva ◽

Anselmo Cardoso de Paiva ◽

Marcelo Gattass

Keyword(s):

Prostate Cancer ◽

Support Vector Machine ◽

Feature Extraction ◽

Ising Model ◽

Support Vector ◽

Mr Images ◽

Male Population ◽

Prostate Imaging ◽

Wolff Algorithm

Prostate cancer is the second most prevalent type of cancer in the male population worldwide. Prostate imaging tests have adopted for the prevention, diagnosis, and treatment. It is known that early detection increases the chances of an effective treatment, improving the prognosis of the disease. This paper proposes an automatic methodology for prostate lesions detection. It consists of the following steps: Extracting candidates for lesions with Wolff algorithm; feature extraction using the Ising model measures and finally the uses support vector machine in the classification of a lesion or healthy tissue. The methodology was validated using a set of 28 exams containing the lesion markings and obtained a sensitivity of 95.92%, specificity of 93.89% and accuracy of 94.16%. These are promising since they were more significant than other methods compared.

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