svm training and classification

Greetings,

I have to classify the input image of my dataset. Based on the below example code (Brain MRI detection), I am doing my project. for classification i have to use fitcsvm(). As i am new to matlab, I dont know how to implement it, because i have to pass features into ClassificationSVM. svmtrain() and svmclassify() are not supporting. please suggest on how can i replace the functions to get my result

example code:

g = graycomatrix(G);

stats = graycoprops(g,'Contrast Correlation Energy Homogeneity');

Contrast = stats.Contrast;

Correlation = stats.Correlation;

Energy = stats.Energy;

Homogeneity = stats.Homogeneity;

Mean = mean2(G);

Standard_Deviation = std2(G);

Entropy = entropy(G);

RMS = mean2(rms(G));

%Skewness = skewness(img)

Variance = mean2(var(double(G)));

a = sum(double(G(:)));

Smoothness = 1-(1/(1+a));

Kurtosis = kurtosis(double(G(:)));

Skewness = skewness(double(G(:)));

% Inverse Difference Movement

m = size(G,1);

n = size(G,2);

in_diff = 0;

for i = 1:m

for j = 1:n

temp = G(i,j)./(1+(i-j).^2);

in_diff = in_diff+temp;

end

end

IDM = double(in_diff);

feat = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];

load Trainset.mat

xdata = meas;

group = label;

svmStruct1 = svmtrain(xdata,group,'KernelFunction', 'linear');

species = svmclassify(svmStruct1,feat,'showplot',false);

if strcmpi(species,'MALIGNANT')

helpdlg(' Malignant Tumor ');

disp(' Malignant Tumor ');

else

helpdlg(' Benign Tumor ');

disp(' Benign Tumor ');

end

Answer 1

[Furkan DEMIR]

Hello.

load Trainset.mat has two file. one of these meas and label.

When I see meas files. I saw 20*13 matrix. what is the meaning. Why the file is 20*13 matrix

[vidhya v]

Hello,

Thank you for your reply.

I was trying to use SVM for image processing in my project. I'm completely new to this stuff and I too don't know abt this code. Actually I took this code from github as my reference. I don't get the result and I dropped the idea of using SVM and went for BPNN. Sorry, I too don't know.

Answer 2

[Mahesh Taparia]

Hi

You can use the function fitcsvm as follows:

SVMModel = fitcsvm(xdata,group,'KernelFunction', 'linear');
[label,score] = predict(SVMModel,feat);

label will give the labels of feat. For more information , you can visit the documentation page.

[vidhya v]

Thank you sir. I will try it