Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique

2013 ◽  
pp. 43-56
Author(s):  
Ahmed Kharrat ◽  
Karim Gasmi ◽  
Mohamed Ben Messaoud ◽  
Nacéra Benamrane ◽  
Mohamed Abid
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Research Work ◽  
Reduction Rate ◽  
Support Vector ◽  
Forward Selection ◽  
Classification Rate ◽  
Brain Images ◽  
Mr Brain Images ◽  
Sequential Floating Forward Selection

A new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images is proposed. The proposed method uses Wavelets Transform (WT) as input module to Genetic Algorithm (GA) and Support Vector Machine (SVM). It segregates MR brain images into normal and abnormal. This contribution employs genetic algorithm for feature selection which requires much lighter computational burden in comparison with Sequential Floating Backward Selection (SFBS) and Sequential Floating Forward Selection (SFFS) methods. A percentage reduction rate of 88.63% is achieved. An excellent classification rate of 100% could be achieved using the support vector machine. The observed results are significantly better than the results reported in a previous research work employing Wavelet Transform and Support Vector Machine.

Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique

2011 ◽  
Vol 3 (2) ◽  
pp. 19-33 ◽  
Author(s):  
Ahmed Kharrat ◽  
Karim Gasmi ◽  
Mohamed Ben Messaoud ◽  
Nacéra Benamrane ◽  
Mohamed Abid
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Research Work ◽  
Reduction Rate ◽  
Support Vector ◽  
Forward Selection ◽  
Classification Rate ◽  
Brain Images ◽  
Input Module ◽  
Mr Brain Images

A new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images is proposed. The proposed method uses Wavelets Transform (WT) as input module to Genetic Algorithm (GA) and Support Vector Machine (SVM). It segregates MR brain images into normal and abnormal. This contribution employs genetic algorithm for feature selection which requires much lighter computational burden in comparison with Sequential Floating Backward Selection (SFBS) and Sequential Floating Forward Selection (SFFS) methods. A percentage reduction rate of 88.63% is achieved. An excellent classification rate of 100% could be achieved using the support vector machine. The observed results are significantly better than the results reported in a previous research work employing Wavelet Transform and Support Vector Machine.

scholarly journals Prediction of dyslexia using support vector machine in distributed environment

2018 ◽  
Vol 7 (4) ◽  
pp. 2795
Author(s):  
Jothi Prabha A ◽  
Bhargavi R ◽  
Ramesh Ragala
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Research Work ◽  
Great Difficulty ◽  
Support Vector ◽  
Learning Disorder ◽  
Distributed Environment ◽  
Brain Images ◽  
Brain Behavior

Dyslexia is a learning disorder characterized by lack of reading and /or writing skills, difficulty in rapid word naming and also poor in spelling. Dyslexic individuals have great difficulty to read and interpret words or letters. Research work is carried out to classify dyslexic from non-dyslexics by various approaches such as machine learning, image processing, understanding the brain behavior through psychology, studying the differences in anatomy of brain. In addition to it several assistive tools are developed to support dyslexics. In this work, brain images are used for screening individuals who have high risk to dyslexia. This work also motivates the application of machine learning in distributed environment. The proposed predictive model uses the machine-learning algorithm Support Vector Machine (SVM). The model is designed in Apache SPARK framework to support voluminous data. The prediction accuracy of 92.5% is achieved using SVM. 

Research on Predicted Model of Least Squares Support Vector Machine Based on Genetic Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 2875-2881 ◽  
Author(s):  
Huai Lin Dong ◽  
Juan Juan Huang ◽  
Zhu Hua Cai ◽  
Qing Feng Wu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Classification Rate ◽  
Financial Investment ◽  
Stock Investment ◽  
Increasing Trend ◽  
Predicted Model ◽  
Stock Indexes

There is huge amount of data with complex uncertainty in the stock market. Meanwhile, efficient stock prediction is important in financial investment. This paper puts forward a classified and predicted model based on least squares support vector machine (LS-SVM) in the background of stock investment. This model preprocesses the input vector of stock indexes using the method of Wilcoxon symbols test and factor analysis, and determines the parameter of LS-SVM based on the genetic algorithm, after that classifies the stocks based on growth rate, then is trained using the stock sample. At last this paper verifies the model with the samples. It also presents a demo to predict the increasing trend of the stock. The result shows that this model owns favorable predicted ability with high correct classification rate.

scholarly journals A Green Computing Supportive Allocation Scheme Utilizing Genetic Algorithm and Support Vector Machine

2019 ◽  
Vol 8 (9S) ◽  
pp. 760-766
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fitness Function ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Support Vector ◽  
Allocation Policy ◽  
Physical Machine ◽  
Best Fit

When a Physical Machine gets a job from user, it intends to complete it at any cost. Virtual Machine (VM) helps to attain maximum completion ratio. The Host to VM ratio increases with the increase in the workload over the system. The allocation policy of VM has ambiguities with leads to an overloaded Physical Machine (PM). This paper aims to reduce the overhead of the PMs. For the allocation, Modified Best Fit Decreasing (MBFD) algorithm is used to check the resources availability. For the allocation, Modified Best Fit Decreasing (MBFD) algorithm is used to check the resources availability. Genetic Algorithm (GA) has been used to optimize the MBFD performance by fitness function. For the cross-validation Polynomial Support Vector Machine (P-SVM) is used. It has been utilized for training and classification and accordingly, parameters, viz. (Service Level Agreement) SLA and Job Completion Ratio (JCR) are evaluated. A comparative analysis has been drawn in this article to depict the research work effectiveness and an improvement of 70% is perceived.

Detection of Amaranthus palmeri sp. Seedlings in Vegetable Farms Using Genetic Algorithm Optimized Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 267-271 ◽  
Author(s):  
W.K Wong ◽  
Muralindran Mariappan ◽  
Ali Chekima ◽  
Manimehala Nadarajan ◽  
Brendan Khoo
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Fine Tuning ◽  
Support Vector ◽  
Amaranthus Palmeri ◽  
Weed Species ◽  
Classification Rate ◽  
Vector Machines

This research is a part of a larger research scope to recognise individual weed species for weed scouting and spot weeding. Support Vector Machines are used to classify the presence of specified weeds(Amaranthus palmeri )by analysing the shape of the weeds. Weed leaves are extracted using image dilation and erosion methods. Several shape feature types were proposed and a total of 59 features were used as the feature pool. The feature selection and fine tuning of the Support Vector Machine are performed using Genetic Algorithm. The outcome is a generalised classifier that enables classification of weed leaves with an average of 90.5% classification rate.

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