machine learning for medical analysis

close all
clc
clear all
[filename,pathname] = uigetfile({'*.*';'*.bmp';'*.tif';'*.gif';'*.png'},'D:\matlab aml\dataset3');
I = imread([pathname,filename]);
figure, imshow(I); title('Brain MRI Image');
I = imresize(I,[200,200]);
% Convert to grayscale
gray = rgb2gray(I);
%  Binarization for segmentation
level = graythresh(I);
img = im2bw(I,level);
figure, imshow(img);title(' Thresholded Image');
% K means Clustering to segment tumor
cform = makecform('srgb2lab');
% Apply the colorform
lab_he = applycform(I,cform);
% Classify the colors in a*b* colorspace using K means clustering.
% Since the image has 3 colors create 3 clusters.
% Measure the distance using Euclidean Distance Metric.
ab = double(lab_he(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
nColors = 1;
[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...
                                      'Replicates',1);
%[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3);
% Label every pixel in tha image using results from K means
pixel_labels = reshape(cluster_idx,nrows,ncols);
%figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');
% Create a blank cell array to store the results of clustering
segmented_images = cell(1,3);
% Create RGB label using pixel_labels
rgb_label = repmat(pixel_labels,[1,1,3]);
for k = 1:nColors
    colors = I;
    colors(rgb_label ~= k) = 0;
    segmented_images{k} = colors;
end
%
figure, imshow(segmented_images{1});title('Objects in Cluster 1');
%figure, imshow(segmented_images{2});title('Objects in Cluster 2');
seg_img = im2bw(segmented_images{1});
figure, imshow(seg_img);title('Segmented Tumor');
% Extract features using DWT
x = double(seg_img);
m = size(seg_img,1);
n = size(seg_img,2);
%signal1 = (rand(m,1));
%winsize = floor(size(x,1));
%winsize = int32(floor(size(x)));
%wininc = int32(10);
%J = int32(floor(log(size(x,1))/log(2)));
%Features = getmswpfeat(signal,winsize,wininc,J,'matlab');
%m = size(img,1);
%signal = rand(m,1);
signal1 = seg_img(:,:);
%Feat = getmswpfeat(signal,winsize,wininc,J,'matlab');
%Features = getmswpfeat(signal,winsize,wininc,J,'matlab');
[cA1,cH1,cV1,cD1] = dwt2(signal1,'db4');
[cA2,cH2,cV2,cD2] = dwt2(cA1,'db4');
[cA3,cH3,cV3,cD3] = dwt2(cA2,'db4');
DWT_feat = [cA3,cH3,cV3,cD3];
G = pca(DWT_feat);
whos DWT_feat
whos G
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];
% Normalize features to have zero mean and unit variance
%feat = real(feat);
%feat = (feat-mean(feat(:)));
%feat=feat/std(feat(:));
%DWT_Features = cell2mat(DWT_feat);
%mean = mean(DWT_feat(:));
%feat1 = getmswpfeat(signal1,20,2,2,'matlab');
%signal2 = rand(n,1);
%feat2 = getmswpfeat(signal2,200,6,2,'matlab');
%feat2 = getmswpfeat(signal2,20,2,2,'matlab');
% Combine features
%features = [feat1;feat2];
% Apply PCA to reduce dimensionality
%coeff = pca(features);
% Check dimensionality reduction
%whos features
%whos coeff
%load Trainset.mat
a = magic(4);
b = ones(2, 4) * -5.7;
c = [8 6 4 2];
save -ascii train.m a b c
clear a b c
 load train.m -ascii
 xdata = meas();
 group = label;
 %svmStruct = svmtrain(xdata,group,'showplot',false);
 % species = svmclassify(svmStruct,feat)
 svmStruct1 = fitcsvm(xdata,group,'kernel_function', 'linear');
 %cp = classperf(group);
 %feat1 = [0.1889 0.9646 0.4969 0.9588 31.3445 53.4054 3.0882 6.0023 1.2971e+03 1.0000 4.3694 1.5752 255];
% feat2 = [ 0.2790 0.9792 0.4229 0.9764 64.4934 88.6850 3.6704 8.4548 2.3192e+03 1.0000 1.8148 0.7854 255];
 species = ClassificationSVM(svmStruct1,feat,'showplot',false);
 %classperf(cp,species,feat2);
 %classperf(cp,feat2);
% Accuracy = cp.CorrectRate;
% Accuracy = Accuracy*100
% Polynomial Kernel
% svmStruct2 = svmtrain(xdata,group,'Polyorder',2,'Kernel_Function','polynomial');
 %species_Poly = svmclassify(svmStruct2,feat,'showplot',false)
 
% Quadratic Kernel
%svmStruct3 = svmtrain(xdata,group,'Kernel_Function','quadratic');
%species_Quad = svmclassify(svmStruct3,feat,'showplot',false)
 
% RBF Kernel
%svmStruct4 = svmtrain(xdata,group,'RBF_Sigma', 3,'Kernel_Function','rbf','boxconstraint',Inf);
%species_RBF = svmclassify(svmStruct4,feat,'showplot',false)
% To plot classification graphs, SVM can take only two dimensional data
data1   = [meas(:,1), meas(:,2)];
newfeat = [feat(:,1),feat(:,2)];
pause
%close all
svmStruct1_new = fitcsum(data1,group,'kernel_function', 'linear','showplot',false);
species_Linear_new = ClassificationSVM(svmStruct1_new,newfeat,'showplot',false);
% Multiple runs for accuracy highest is 90%
%load Trainset.mat
a = magic(4);
b = ones(2, 4) * -5.7;
c = [8 6 4 2];
save -ascii train.m a b c
clear a b c
 load train.m -ascii
%data   = [meas(:,1), meas(:,2)];
data = meas();
groups = ismember(label,'BENIGN   ');
groups = ismember(label,'MALIGNANT');
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
%svmStruct = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
classes = ClassificationSVM(svmStruct,data(test,:),'showplot',false);
classperf(cp,classes,test);
Accuracy_Classification = cp.CorrectRate.*100;
sprintf('Accuracy of Linear kernel is: %g%%',Accuracy_Classification)
%% Accuracy with RBF
svmStruct_RBF = fitcsvm(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
classes2 = ClassificationSVM(svmStruct_RBF,data(test,:),'showplot',false);
classperf(cp,classes2,test);
Accuracy_Classification_RBF = cp.CorrectRate.*100;
sprintf('Accuracy of RBF kernel is: %g%%',Accuracy_Classification_RBF)
%% Accuracy with Polynomial
svmStruct_Poly = fitcsvm(data(train,:),groups(train),'Polyorder',2,'Kernel_Function','polynomial');
classes3 = ClassificationSVM(svmStruct_Poly,data(test,:),'showplot',false);
classperf(cp,classes3,test);
Accuracy_Classification_Poly = cp.CorrectRate.*100;
sprintf('Accuracy of Polynomial kernel is: %g%%',Accuracy_Classification_Poly)
%%
% 5 fold cross validation
% 5 fold cross validation
load Normalized_Features.mat
 xdata = norm_feat;
 group = norm_label;
indicies = crossvalind('Kfold',label,5);
cp = classperf(label);
for i = 1:length(label)
    test = (indicies==i);train = ~ test;
    svmStruct = fitcsvm(xdata(train,:),group(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
    classes = ClassificationSVM(svmStruct,xdata(test,:),'showplot',false);
    %class = svmclassify(meas(test,:),meas(train,:),label(train,:));
    classperf(cp,classes,test);
end
%Accu = cp.ClassifiedRate;
Accuracy = cp.CorrectRate;
%sprintf('Accuracy of classification with 5 fold cross validation is: %g%%',Accu*100)
%% Accuracy for normalized features
%load Normalized_Features.mat
% xdata = norm_feat;
% data   = [xdata(:,1), xdata(:,2)];
%groups = ismember(label,'BENIGN   ');
%groups = ismember(label,'MALIGNANT');
%[train,test] = crossvalind('HoldOut',groups);
%cp = classperf(groups);
%svmStruct = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
%svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
%classes = svmclassify(svmStruct,data(test,:),'showplot',false);
%classperf(cp,classes,test);
%Accuracy_New = cp.CorrectRate.*100;
%sprintf('Accuracy of classification is: %g%%',Accuracy_New);
%% Hold out on normalized features highest is 70%
%load Normalized_Features.mat
% xdata = norm_feat;
%data = norm_feat;
% group = norm_label;
% groups = ismember(label,'BENIGN   ');
%groups = ismember(label,'MALIGNANT');
%[train,test] = crossvalind('HoldOut',groups);
%cp = classperf(groups);
%svmStruct = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
%svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
%classes = svmclassify(svmStruct,data(test,:),'showplot',false);
%classperf(cp,classes,test);
%Accuracy_Classification = cp.CorrectRate.*100;
%sprintf('Accuracy of classification is: %g%%',Accuracy_Classification)

this is my code and my error in meas function what is the problem

[Walter Roberson]

In some places you are using meas() as if it is a function, and other places you are using it as if it is a variable, such as meas(:,1)

meas is not provided by Mathworks and you do not provide source for it and you never store into it, so we do not know what it is.

[Mohamed Nasr]

please you have ANN code not Deep learning to brain tumor classification

[Walter Roberson]

I do not, but there have been quite a number of postings on the topic. https://www.mathworks.com/matlabcentral/answers/?term=tag%3A%22tumor%22

[Mohamed Nasr]

thanx