Using SVM on Test data gives the same result for different classes

Hello,

I am using SVM to classify breast tissues. Training was performed on 16 Benign and 16 Malignant tissues. However, when I applied my model on my test data set which had 4 benign and 4 malignant images, all the results show me the same output(benign) for all the test images. The Labels.xlsx file shows the feature extracted by the Gray-Level Cooccurence Matrix(GLCM) of the 32 images and is used as the training set.Classes 1 and 2 corresponds to Benign and Malignant tissues respectively. Ben 1 and Ben5 are one of Benign and Malignant test image respectively. Please find my code below: Any help would be very much appreciated.

inp=input('Enter Image:');
    i=imread(inp);

    try
    i=rgb2gray(i);
end
    z=im2bw(i,0.1);
figure(2)
imshow(z);title('Original B&W')
info=regionprops(z);
a=cat(1,info.Area);
[m,l]=max(a);
X=info(l).Centroid;
bw2=bwselect(z,X(1),X(2),8);
i=immultiply(i,bw2);
figure(3)
imshow(i);
title('Getting the Breast and Muscle')

%% Deleting Black Ground

% We will delete the black corners
% So that we can select the muscle
% using bwselect
% convert to B&W first time
[x,y]=size(z);
tst1=zeros(x,y);

% detect empty rows
r1=[];
m=1;
for j=1:x
    if z(j,:)==tst1(j,:)
        r1(m)=j;
        m=m+1;
    end
end

% detect empty columns
r2=[];
m=1;
for j=1:y
    if z(:,j)==tst1(:,j)
        r2(m)=j;
        m=m+1;
    end
end

% Deleting
i(:,r2)=[];
i(r1,:)=[];

figure(4)
imshow(i);title('after deleting background');


%% Deleting the Muscle

if i(1,1)~=0
    c=3;
    r=3;
else
    r=3;
    c=size(i,2)-3;
end

z2=im2bw(i,0.5);
bw3=bwselect(z2,c,r,8);
bw3=~bw3;
ratio=min(sum(bw3)/sum(z2));
if ratio>=1
    i=immultiply(i,bw3);
else
    z2=im2bw(i,0.75);
    bw3=bwselect(z2,c,r,8);
    ratio2=min(sum(bw3)/sum(z2));
    if round(ratio2)==0
        lvl=graythresh(i);
        z2=im2bw(i,1.75*lvl);
        bw3=bwselect(z2,c,r,8);
        bw3=~bw3;
        i=immultiply(i,bw3);
    else
        bw3=~bw3;
        i=immultiply(i,bw3);
    end
end
figure(5)
imshow(i)
title('Getting only the Breast')


J = imnoise(i,'salt & pepper', 0.02);
NoisyImage=J;
[R C P]=size(NoisyImage);
OutImage=zeros(R,C);
figure;
% imshow(J);
Zmin=[];
Zmax=[];
Zmed=[];

for i=1:R
    for j=1:C
               if (i==1 & j==1)
                   
                   
                        
                        
          % for right top corner[8,7,6]
                elseif (i==1 & j==C)
                        
                        
			% for bottom left corner[2,3,4]
                elseif (i==R & j==1)
                        
                        
        		     % for bottom right corner[8,1,2]

                elseif (i==R & j==C)
                        
                        
        		    %for top edge[8,7,6,5,4]

                elseif (i==1)
		      			 	
		      			 
                     		      			% for right edge[2,1,8,7,6]

                elseif (i==R)
		      			 
		      			% // for bottom edge[8,1,2,3,4]
		      			 
                elseif (j==C)
		      			 
						 
                         %// for left edge[2,3,4,5,6]
						 
                elseif (j==1)
                    
						 
                            
                         
						 
               else
                    
                   
						 	
                             SR1 = NoisyImage((i-1),(j-1));
 							 SR2 = NoisyImage((i-1),(j));
    						 SR3 = NoisyImage((i-1),(j+1));
 							 SR4 = NoisyImage((i),(j-1));
                             SR5 = NoisyImage(i,j);
 							 SR6 = NoisyImage((i),(j+1));
 							 SR7 = NoisyImage((i+1),(j-1));
 							 SR8 = NoisyImage((i+1),(j));
 							 SR9 = NoisyImage((i+1)),((j+1));
                             TempPixel=[SR1,SR2,SR3,SR4,SR5,SR6,SR7,SR8,SR9];
                             Zxy=NoisyImage(i,j);
                             Zmin=min(TempPixel);
                             Zmax=max(TempPixel);
                             Zmed=median(TempPixel);
                             A1 = Zmed - Zmin;
                             A2 = Zmed - Zmax;
                         
                             if A1 > 0 && A2 < 0
                             
                                  %   go to level B
                                  B1 = Zxy - Zmin;
                                  B2 = Zxy - Zmax;
                                  if B1 > 0 && B2 < 0
                                      OutImage(i,j)= Zxy;
                                  else
                                      OutImage(i,j)= Zmed;

                                  end
                             else
                                 
                                 if ((R > 4 && R < R-5) && (C > 4 && C < C-5))
                                 
                                 S1 = NoisyImage((i-1),(j-1));
                                 S2 = NoisyImage((i-2),(j-2));
                                 S3 = NoisyImage((i-1),(j));
                                 S4 = NoisyImage((i-2),(j));
                                 S5 = NoisyImage((i-1),(j+1));
                                 S6 = NoisyImage((i-2),(j+2));
                                 S7 = NoisyImage((i),(j-1));
                                 S8 = NoisyImage((i),(j-2));

                                 S9 = NoisyImage(i,j);
                                 S10 = NoisyImage((i),(j+1));
                                 S11 = NoisyImage((i),(j+2));
                                 S12 = NoisyImage((i+1),(j-1));
                                 S13 = NoisyImage((i+2),(j-2));
                                 S14 = NoisyImage((i+1),(j));
                                 S15 = NoisyImage((i+2),(j));
                                 S16 = NoisyImage((i+1)),((j+1));
                                 S17 = NoisyImage((i+2)),((j+2));
                                 TempPixel2=[S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12,S13,S14,S15,S16,S17];
                                 Zmed2=median(TempPixel2);
                                 OutImage(i,j)= Zmed2;
                                 else
                                     
                                 OutImage(i,j)= Zmed;

                                 end

                             end         
                     
          end    
      
    end
end
imshow(OutImage,[]);
title('Adaptive Median Filter')
disp('exit');

%%GLCM Feature Extraction
% Y=rgb2gray(OutImage);
Y=double(OutImage);

    glcm2=graycomatrix(Y);
    stats = GLCM_Features1(glcm2,0);
    stats=stats';
    ExtractedFeaturesm16=stats;
% save('ExtractedFeaturesm16.mat')
    statsTable = struct2table(stats);
%     statsArray = table2array(statsTable);
 statsArray1 = table2array(statsTable);
% statsArray'


   
   TestSet=statsArray1;

   Training=Labels1(1:32,1:22);
   class=Labels1(:,23);
   
   SVMmodel=fitcsvm(Training,class);
   result = predict(SVMmodel, TestSet);
  
   if result == 1
            helpdlg(' Benign ');
            disp(' Benign Tissue ');
        else
            helpdlg(' Malignant ');
            disp('Malignant Tissue');
   end