scholarly journals Abnormality Detection in Brain CT Image Using Support Vector Machine

10.29007/jsfg ◽  
2018 ◽  
Author(s):  
Bhavna Sharma ◽  
Priyanka Mitra
Keyword(s):  
Support Vector Machine ◽  
Ct Images ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Normal Brain ◽  
Brain Images ◽  
Brain Ct ◽  
Automated Method

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

scholarly journals Security Challenges and Application for Underwater Wireless Sensor Network

10.29007/ctsn ◽  
2018 ◽  
Author(s):  
Sarvesh Kumar Kumar ◽  
Bersha Kumari ◽  
Harshita Chawla
Keyword(s):  
Ct Images ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Normal Brain ◽  
Brain Images ◽  
Brain Ct ◽  
Automated Method ◽  
Occurrence Matrix

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

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.

scholarly journals Brain Tumor Segmentation from Multimodal MRI Data based on GLCM and SVM Classifier

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Brain Tumor ◽  
Surgical Navigation ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Normal Brain ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Normal Tissues

The segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure.This manuscript per the authors focus on the effects of GLCM texture on brain tumor segmentation. The result is different from the application of the GLCM texture in other types of image processing.The experimental material was a dataset called BraTs2015. The segmented five different labels are normal brain, necrosis, edema, non-enhancing tumor, and enhancing tumor. The proposed model was verified with the Dice coefficient. The result demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

Spectral Classification of Oral and Nasal Snoring Sounds Using a Support Vector Machine

2013 ◽  
Vol 17 (4) ◽  
pp. 611-621 ◽  
Author(s):  
Tsuyoshi Mikami ◽  
◽  
Yohichiro Kojima ◽  
Kazuya Yonezawa ◽  
Masahito Yamamoto ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Upper Airway ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Spectral Classification ◽  
Simple Method ◽  
Oral Breathing ◽  
Sleep Condition

Since oral breathing during sleep tends to make the upper airway more collapsible, loud snoring caused by oral breathing is found in many sleep apnea/hypopnea patients and should be detected in the earlier stage. But unfortunately we cannot know our own sleep condition or snoring. Thus, a simple method that can detect oral snoring makes it possible to become a useful technique to develop a home medical device. For such purpose, we adopt a Support Vector Machine (SVM) classifier so as to classify oral and nasal snoring sounds based on the spectral properties. Fifteen subjects are asked to simulate snoring with oral and nasal breath respectively and the sounds are recorded with a linear sound recorder. We adopted seven kernel functions (linear, polynomial, sigmoid, Gaussian, Laplacian, chisquare, and Kullback-Leibler) for SVM-based spectral classification. As a result, over 95% of snoring sounds are successfully classified under the various cross validation test.

scholarly journals Support Vector Machine and Probability Neural Networks in a Device-Free Passive Localization (DFPL) Scenario

2012 ◽  
Vol 17 (4) ◽  
pp. 9-16 ◽  
Author(s):  
Gabriel Deak ◽  
Kevin Curran ◽  
Joan Condell ◽  
Daniel Deak ◽  
Piotr Kiedrowski
Keyword(s):  
Support Vector Machine ◽  
Probabilistic Neural Network ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Quadratic ◽  
Wireless Signal ◽  
Device Free ◽  
Tracking Devices ◽  
Passive Localization

Abstract The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP), and a Probabilistic Neural Network (PNN) in order to detect movement based on changes in the wireless signal strength.

scholarly journals The effect of gamma value on support vector machine performance with different kernels

2020 ◽  
Vol 10 (5) ◽  
pp. 5497
Author(s):  
Intisar Shadeed Al-Mejibli ◽  
Jwan K. Alwan ◽  
Dhafar Hamed Abd
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Kernel Functions ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Performance ◽  
Rbf Kernel ◽  
The Impact

Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.

scholarly journals Autonomous Monitoring of Line-to-Line Faults in Photovoltaic Systems by Feature Selection and Parameter Optimization of Support Vector Machine Using Genetic Algorithms

2020 ◽  
Vol 10 (16) ◽  
pp. 5527 ◽  
Author(s):  
Aref Eskandari ◽  
Jafar Milimonfared ◽  
Mohammadreza Aghaei ◽  
Angèle H.M.E. Reinders
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Parameter Optimization ◽  
Kernel Functions ◽  
Normal Operation ◽  
Support Vector ◽  
Svm Classifier ◽  
Pv Systems ◽  
High Impedance ◽  
High Impedance Fault

Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current (DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current–Voltage (I–V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively.

A simplified framework for the detection of intracranial hemorrhage in CT brain images using deep learning

2021 ◽  
Vol 17 ◽  
Author(s):  
Praveen K ◽  
Sasikala M ◽  
Janani A ◽  
Nijisha Shajil ◽  
Hari Nishanthi V
Keyword(s):  
Deep Learning ◽  
Intracranial Hemorrhage ◽  
Feature Selection Method ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Brain Images ◽  
Ct Brain ◽  
Ct Brain Images

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is utmost important to avoid untoward incidents that may even lead to death.Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. Methods: Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method, secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored and finally, a feature selection method, Principal Component Analysis (PCA) has been introduced in the AlexNet-SVM classifier model and its efficacy is explored.These models were trained and tested on two different sets of CT images, one containing the original images without preprocessing and another set consisting of preprocessed images. Results: The modified AlexNet-SVM classifier has shown an improved performance in comparison to the other investigated frameworks and has achieved a classification accuracy of 99.86%, sensitivity and specificity of 0.9986 for the detection of ICH in brain CT images. Conclusion: This research has given an overview of a simple and efficient framework for the classification of hemorrhage and non-hemorrhage images. Also, the proposed simplified deep learning framework manifests its ability as a screening tool to assist the radiological trainees for the accurate detection of ICH.

Development of a Predictive Model for Textual Data Using Support Vector Machine Based on Diverse Kernel Functions Upon Sentiment Score Analysis

2021 ◽  
Vol 10 (2) ◽  
pp. 1-20
Author(s):  
Sheik Abdullah A. ◽  
Akash K. ◽  
Bhubesh K. R. A. ◽  
Selvakumar S.
Keyword(s):  
Support Vector Machine ◽  
Predictive Model ◽  
Model Development ◽  
Research Work ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Level Data ◽  
Score Analysis ◽  
Sentiment Score

This research work specifically focusses on the development of a predictive model for movie review data using support vector machine (SVM) classifier with its improvisations using different kernel functions upon sentiment score estimation. The predictive model development proceeds with user level data input with the data processing with the data stream for analysis. Then formal calculation of TF-IDF evaluation has been made upon data clustering using simple k-means algorithm. Once the labeled data has been sorted out, then the SVM with kernel functions corresponding to linear, sigmoid, rbf, and polynomial have been applied over the clustered data with specific parameter setting for each type of library functions. Performance of each of the kernels has been measured using precision, recall, and F-score values for each of the specified kernel, and from the analysis, it has been found that sentiment analysis using SVM linear kernel with sentiment score analysis has been found to provide an improved accuracy of about 91.18%.

scholarly journals Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods

2021 ◽  
Vol 10 (1) ◽  
pp. 290-298
Author(s):  
Lakshmana Kumar Ramasamy ◽  
Seifedine Kadry ◽  
Sangsoon Lim
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Optimization Techniques ◽  
Supervised Machine Learning ◽  
Classification Task ◽  
Support Vector ◽  
Svm Classifier ◽  
Selection Of

Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.

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