svm training and classification

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vidhya v
vidhya v 2020-3-19
评论： vidhya v 2020-12-10
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

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采纳的回答

Furkan DEMIR
Furkan DEMIR 2020-12-10
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
  1 个评论
vidhya v
vidhya v 2020-12-10
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.

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更多回答（1 个）

Mahesh Taparia
Mahesh Taparia 2020-3-23
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.

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