Research on the Segmentation of MRI Image Based on Immune Support Vector Machine

2007 ◽  
Author(s):  
Lei Guo ◽  
Youxi Wu ◽  
Xuena Liu ◽  
Weili Yan
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Mri Image

scholarly journals Tumor Detection and Classification of MRI Brain Image using Support Vector Machine (SVM)

2021 ◽  
pp. 444-448
Author(s):  
D. Harish Kumar ◽  
D. Ruby
Keyword(s):  
Support Vector Machine ◽  
Malignant Tumors ◽  
Support Vector ◽  
Benign Tumors ◽  
Low Grade ◽  
Normal Brain ◽  
Cerebral Tumor ◽  
Brain Image ◽  
Svm Algorithm ◽  
Mri Image

Brain is one of the most complex organs in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low-grade and malignant tumors or high-grade. Benign tumors are non-cancerous tumor and they do not spread to other tissues or organs. Malignant tumors are cancerous tissue and they can easily spread to other tissues or organs. Proposed system is to differentiate between normal brain and tumor brain (benign or malign). Also, the proposed system predicts brain tumor from MRI image classification system is based on extracting useful MRI features for diagnosing the medical MRI images. The benefits of using SVM is nevertheless of the image brightness or rotation of the MRI image, it also provides huge number of strong features that can be automatically prepared well to be suitable for MRI classification. Support Vector Machine (SVM) algorithm is used to predict the diseases accurately from MRI (Magnetic Resonance Imaging) scan images. SVM algorithm is the used for the purpose of classifying the image datasets and to predict the disease by itself for those matching the images to enhance a comprehensive set of quantitative measurements among several influential on various brain image databases.

scholarly journals MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bo Feng ◽  
Meihua Zhang ◽  
Hanlin Zhu ◽  
Lingang Wang ◽  
Yanli Zheng
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Support Vector ◽  
B Values ◽  
Svm Algorithm ◽  
Malignant Group ◽  
Benign Group ◽  
Mri Image

This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences ( P > 0.05 ) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group ( P < 0.01 ). The ADC value with different b values in the benign group was significantly lower than that of the malignant group ( P < 0.01 ). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

scholarly journals Segmentation of MRI images to detect multiple sclerosis using non-parametric, non-uniform intensity normalization and support vector machine methods

2021 ◽  
Vol 13 (1) ◽  
pp. 68-74
Author(s):  
Mohammad Moghadasi ◽  
Gabor Fazekas
Keyword(s):  
Multiple Sclerosis ◽  
Support Vector Machine ◽  
Principal Component ◽  
Bias Field ◽  
Definitive Treatment ◽  
Support Vector ◽  
Average Accuracy ◽  
The Central Nervous System ◽  
Intensity Normalization ◽  
Mri Image

Multiple sclerosis (MS) is an inflammatory, chronic, persistent, and destructive disease of the central nervous system whose cause is not yet known but can most likely be the result of a series of unknown environmental factors reacting with sensitive genes. MRI is a method of neuroimaging studies that results in better image contrast in soft tissue. Due to the unknown cause of MS and the lack of definitive treatment, early diagnosis of this disease is important. MRI image segmentation is used to identify MS plaques. MRI images have an image error that is often called non-uniform light intensity. There are several ways to correct non-uniform images. One of these methods is Nonparametric Non-uniform intensity Normalization (N3). This method sharpens the histogram. The aim of this study is to reduce the effect of bias field on the MRI image using N3 algorithm and pixels of MRI images clustered by k-means algorithm. The dimensionality of the data is reduced by Principal Component Analysis (PCA) algorithm and then the segmentation is done by Support Vector Machine (SVM) algorithm. Results show that using the proposed system could diagnose multiple sclerosis with an average accuracy of 93.28%.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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