hiii,can anyone plz help,how to create a confusion matrix for this code.Basically the it uses SVM to classify in 6 different classes OF SKIN CANCER.Please help by providing the code to write the confusion matrix for the same.

function varargout = main(varargin)

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @main_OpeningFcn, ...

'gui_OutputFcn', @main_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

if nargin && ischar(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

gui_mainfcn(gui_State, varargin{:});

end

% End initialization code - DO NOT EDIT

% --- Executes just before main is made visible.

function main_OpeningFcn(hObject, eventdata, handles, varargin)

% This function has no output args, see OutputFcn.

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% varargin command line arguments to main (see VARARGIN)

% Choose default command line output for main

% Update handles structure

guidata(hObject, handles);

axes(handles.axes1); axis off

% UIWAIT makes main wait for user response (see UIRESUME)

% uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line.

function varargout = main_OutputFcn(hObject, eventdata, handles)

% varargout cell array for returning output args (see VARARGOUT);

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure

%varargout{1} = handles.output;

% --- Executes on button press in pushbutton1.

function pushbutton1_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton1 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

[filename, pathname, filterindex]=uigetfile( ...

{'*.jpg','JPEG File (*.jpg)'; ...

'*.*','Any Image file (*.*)'}, ...

'Pick an image file');

var=strcat(pathname,filename);

k=imread(var);

handles.YY = k;

set(handles.edit1,'String',var);

z=k;

guidata(hObject,handles);

%guidata(hObject,handles);

axes(handles.axes1);

imshow(k);

% set(handles.axes1);

title('Input Image');

set(handles.pushbutton1,'enable','off');

set(handles.pushbutton16,'enable','on');

% --- Executes on button press in pushbutton2.

function pushbutton2_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton2 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

k=handles.YY;

j=rgb2gray(k);

handles.XX=j;

figure,subplot(2,2,2),imshow(j);title('gray Image');

set(handles.pushbutton1,'enable','off');

set(handles.pushbutton2,'enable','off');

set(handles.pushbutton4,'enable','on');

% --- Executes on button press in pushbutton3.

function pushbutton3_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton3 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

str=get(handles.edit1,'String');

I = imread(str);

h = ones(5,5)/25;

I2 = imfilter(I,h);

figure

imshow(I2)

title('Filtered Image')

set(handles.pushbutton3,'enable','off');

set(handles.pushbutton12,'enable','on');

% --- Executes on button press in pushbutton4.

function pushbutton4_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton4 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

ei=25;

st=35;

%k=10

k=ei*st;

I=handles.YY;

%I = imread('1.jpg');

%h=filter matrx

h = ones(ei,st) / k;

I1 = imfilter(I,h,'symmetric');

IG=rgb2gray(I1);

%Converting to BW

I11 = imadjust(IG,stretchlim(IG),[]);

level = graythresh(I11);

BWJ = im2bw(I11,level);

dim = size(BWJ)

IN=ones(dim(1),dim(2));

BW=xor(BWJ,IN); %inverting

figure,subplot(2,2,2), imshow(BW), title('Black and White');

set(handles.pushbutton1,'enable','off');

set(handles.pushbutton2,'enable','off');

set(handles.pushbutton3,'enable','on');

set(handles.pushbutton4,'enable','off');

% --- Executes on button press in pushbutton5.

function pushbutton5_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton5 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% --- Executes on button press in pushbutton6.

function pushbutton6_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton6 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

%I=imread('cancer.bmp');

I=handles.YY;

[y,x,z]=size(I);

myI=double(I);

H=zeros(y,x);

S=H;

HS_I=H;

for i=1:x

for j=1:y

HS_I(j,i)=((myI(j,i,1)+myI(j,i,2)+myI(j,i,3))/3);

S(j,i)=1-3*min(myI(j,i,:))/(myI(j,i,1)+myI(j,i,2)+myI(j,i,3));

if ((myI(j,i,1)==myI(j,i,2))&(myI(j,i,2)==myI(j,i,3)))

Hdegree=0;

else

Hdegree=acos(0.5*(2*myI(j,i,1)-myI(j,i,2)-myI(j,i,3))/((myI(j,i,1)-myI(j,i,2))^2+(myI(j,i,1)-myI(j,i,3))*(myI(j,i,2)-myI(j,i,3)))^0.5);

end

if (myI(j,i,2)>=myI(j,i,3))

H(j,i)=Hdegree;

else

H(j,i)=(2*pi-Hdegree);

end

end

end

Hth1=0.9*2*pi; Hth2=0.1*2*pi;

Nred=0;

for i=1:x

for j=1:y

if ((H(j,i)>=Hth1)||(H(j,i)<=Hth2))

Nred=Nred+1;

end

end

end

Ratio=Nred/(x*y);

if (Ratio>=0.6)

Red=1

else

Red=0

end

HS_I=uint8(HS_I);

figure(1);

imshow(I);

figure(2);

imshow(HS_I);

% --- Executes on button press in pushbutton7.

function pushbutton7_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton7 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

createffnn

% --- Executes on button press in pushbutton8.

function pushbutton8_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton8 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% NET = trainnet(net,IMGDB);

str=get(handles.edit1,'String');

I1 = imread(str);

I = im2double(I1);

HSV = rgb2hsv(I);

H = HSV(:,:,1); H = H(:);

S = HSV(:,:,2); S = S(:);

V = HSV(:,:,3); V = V(:);

idx = kmeans([H S V], 4);

imshow(I1);

figure,imshow(ind2rgb(reshape(idx, size(I,1), size(I, 2)), [0 0 1; 0 0.8 0]))

% --- Executes on button press in pushbutton9.

function pushbutton9_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton9 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% --- Executes on button press in pushbutton10.

function pushbutton10_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton10 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

function edit1_Callback(hObject, eventdata, handles)

% hObject handle to edit1 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit1 as text

% str2double(get(hObject,'String')) returns contents of edit1 as a double

% --- Executes during object creation, after setting all properties.

function edit1_CreateFcn(hObject, eventdata, handles)

% hObject handle to edit1 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))

set(hObject,'BackgroundColor','white');

end

function [ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum )

if nargin < 2

clusterNum = 2; % number of cluster

end

[row, col] = size(img);

expoNum = 2; % fuzzification parameter

epsilon = 0.001; % stopping condition

mat_iter = 100; % number of maximun iteration

Upre = rand(row, col, clusterNum);

dep_sum = sum(Upre, 3);

dep_sum = repmat(dep_sum, [1,1, clusterNum]);

Upre = Upre./dep_sum;

center = zeros(clusterNum,1);

for i=1:clusterNum

center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));

end

pre_obj_fcn = 0;

for i=1:clusterNum

pre_obj_fcn = pre_obj_fcn + sum(sum((Upre(:,:,i) .*img - center(i)).^2));

end

%fprintf('Initial objective fcn = %f\n', pre_obj_fcn);

for iter = 1:mat_iter

Unow = zeros(size(Upre));

for i=1:row

for j=1:col

for uII = 1:clusterNum

tmp = 0;

for uJJ = 1:clusterNum

disUp = abs(img(i,j) - center(uII));

disDn = abs(img(i,j) - center(uJJ));

tmp = tmp + (disUp/disDn).^(2/(expoNum-1));

end

Unow(i,j, uII) = 1/(tmp);

end

end

end

now_obj_fcn = 0;

for i=1:clusterNum

now_obj_fcn = now_obj_fcn + sum(sum((Unow(:,:,i) .*img - center(i)).^2));

end

% fprintf('Iter = %d, Objective = %f\n', iter, now_obj_fcn);

if max(max(max(abs(Unow-Upre))))<epsilon || abs(now_obj_fcn - pre_obj_fcn)<epsilon

break;

else

Upre = Unow.^expoNum;

for i=1:clusterNum

center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));

end

pre_obj_fcn = now_obj_fcn;

end

end

% --- Executes on button press in pushbutton11.

function pushbutton11_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton11 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% --- Executes on button press in pushbutton12.

function pushbutton12_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton12 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

str=get(handles.edit1,'String');

I1 = imread(str);

str=rgb2gray(I1);

imwrite(str,'img.tif','tiff');

img = double(imread('img.tif'));

clusterNum = 2;

[ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum );

figure;

subplot(2,2,1); imshow(img,[]);

for i=1:clusterNum

subplot(2,2,i+1);

imshow(Unow(:,:,i),[]);

imwrite(Unow(:,:,i),'seg.jpg');

end

imshow('seg.jpg'); title('Segmented Image');

set(handles.pushbutton13,'enable','on');

set(handles.pushbutton12,'enable','off');

% --- Executes on button press in pushbutton13.

function pushbutton13_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton13 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

s=get(handles.edit1,'String');

I = imread(s);

I=rgb2gray(I);

glcms = graycomatrix(I);

getData()

% Derive Statistics from GLCM

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

Contrast = stats.Contrast;

Correlation = stats.Correlation;

Energy = stats.Energy;

Homogeneity = stats.Homogeneity;

seg_img=imread('seg.jpg');

Mean = mean2(seg_img);

Standard_Deviation = std2(seg_img);

Entropy = entropy(seg_img);

RMS = mean2(rms(seg_img));

%Skewness = skewness(img)

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

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

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

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

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

% Inverse Difference Movement

m = size(seg_img,1);

n = size(seg_img,2);

in_diff = 0;

for i = 1:m

for j = 1:n

temp = seg_img(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];

img = imread(s);

gaborArray = gaborFilterBank(5,8,39,39); % Generates the Gabor filter bank

featureVector = gaborFeatures(img,gaborArray,4,4);

disp('Gabor Feature');

featureVector

save('Gaborfeature.mat','featureVector');

% GLCM2 = graycomatrix(I,'Offset',[2 0;0 2]);

% features = GLCM_Features1(GLCM2,0)

set(handles.pushbutton14,'enable','on');

set(handles.pushbutton13,'enable','off');

msgbox('Feature Extracted Done');

clear all;

function [out] = GLCM_Features1(glcmin,pairs)

if ((nargin > 2) || (nargin == 0))

error('Too many or too few input arguments. Enter GLCM and pairs.');

elseif ( (nargin == 2) )

if ((size(glcmin,1) <= 1) || (size(glcmin,2) <= 1))

error('The GLCM should be a 2-D or 3-D matrix.');

elseif ( size(glcmin,1) ~= size(glcmin,2) )

error('Each GLCM should be square with NumLevels rows and NumLevels cols');

end

elseif (nargin == 1)

pairs = 0;

if ((size(glcmin,1) <= 1) || (size(glcmin,2) <= 1))

error('The GLCM should be a 2-D or 3-D matrix.');

elseif ( size(glcmin,1) ~= size(glcmin,2) )

error('Each GLCM should be square with NumLevels rows and NumLevels cols');

end

end

format long e

if (pairs == 1)

newn = 1;

for nglcm = 1:2:size(glcmin,3)

glcm(:,:,newn) = glcmin(:,:,nglcm) + glcmin(:,:,nglcm+1);

newn = newn + 1;

end

elseif (pairs == 0)

glcm = glcmin;

end

size_glcm_1 = size(glcm,1);

size_glcm_2 = size(glcm,2);

size_glcm_3 = size(glcm,3);

out.autoc = zeros(1,size_glcm_3);

out.contr = zeros(1,size_glcm_3);

out.corrm = zeros(1,size_glcm_3);

out.corrp = zeros(1,size_glcm_3);

out.cprom = zeros(1,size_glcm_3);

out.cshad = zeros(1,size_glcm_3);

out.dissi = zeros(1,size_glcm_3);

out.energ = zeros(1,size_glcm_3);

out.entro = zeros(1,size_glcm_3);

out.homom = zeros(1,size_glcm_3);

out.homop = zeros(1,size_glcm_3);

out.maxpr = zeros(1,size_glcm_3);

out.sosvh = zeros(1,size_glcm_3);

out.savgh = zeros(1,size_glcm_3);

out.svarh = zeros(1,size_glcm_3);

out.senth = zeros(1,size_glcm_3);

out.dvarh = zeros(1,size_glcm_3);

out.denth = zeros(1,size_glcm_3);

out.inf1h = zeros(1,size_glcm_3);

out.inf2h = zeros(1,size_glcm_3);

out.indnc = zeros(1,size_glcm_3);

out.idmnc = zeros(1,size_glcm_3);

glcm_sum = zeros(size_glcm_3,1);

glcm_mean = zeros(size_glcm_3,1);

glcm_var = zeros(size_glcm_3,1);

u_x = zeros(size_glcm_3,1);

u_y = zeros(size_glcm_3,1);

s_x = zeros(size_glcm_3,1);

s_y = zeros(size_glcm_3,1);

p_x = zeros(size_glcm_1,size_glcm_3);

p_y = zeros(size_glcm_2,size_glcm_3);

p_xplusy = zeros((size_glcm_1*2 - 1),size_glcm_3);

p_xminusy = zeros((size_glcm_1),size_glcm_3);

hxy = zeros(size_glcm_3,1);

hxy1 = zeros(size_glcm_3,1);

hx = zeros(size_glcm_3,1);

hy = zeros(size_glcm_3,1);

hxy2 = zeros(size_glcm_3,1);

for k = 1:size_glcm_3

glcm_sum(k) = sum(sum(glcm(:,:,k)));

glcm(:,:,k) = glcm(:,:,k)./glcm_sum(k);

glcm_mean(k) = mean2(glcm(:,:,k));

glcm_var(k) = (std2(glcm(:,:,k)))^2;

for i = 1:size_glcm_1

for j = 1:size_glcm_2

out.contr(k) = out.contr(k) + (abs(i - j))^2.*glcm(i,j,k);

out.dissi(k) = out.dissi(k) + (abs(i - j)*glcm(i,j,k));

out.energ(k) = out.energ(k) + (glcm(i,j,k).^2);

out.entro(k) = out.entro(k) - (glcm(i,j,k)*log(glcm(i,j,k) + eps));

out.homom(k) = out.homom(k) + (glcm(i,j,k)/( 1 + abs(i-j) ));

out.homop(k) = out.homop(k) + (glcm(i,j,k)/( 1 + (i - j)^2));

out.sosvh(k) = out.sosvh(k) + glcm(i,j,k)*((i - glcm_mean(k))^2);

out.indnc(k) = out.indnc(k) + (glcm(i,j,k)/( 1 + (abs(i-j)/size_glcm_1) ));

out.idmnc(k) = out.idmnc(k) + (glcm(i,j,k)/( 1 + ((i - j)/size_glcm_1)^2));

u_x(k) = u_x(k) + (i)*glcm(i,j,k);

u_y(k) = u_y(k) + (j)*glcm(i,j,k);

end

end

out.maxpr(k) = max(max(glcm(:,:,k)));

end

for k = 1:size_glcm_3

for i = 1:size_glcm_1

for j = 1:size_glcm_2

p_x(i,k) = p_x(i,k) + glcm(i,j,k);

p_y(i,k) = p_y(i,k) + glcm(j,i,k); % taking i for j and j for i

if (ismember((i + j),[2:2*size_glcm_1]))

p_xplusy((i+j)-1,k) = p_xplusy((i+j)-1,k) + glcm(i,j,k);

end

if (ismember(abs(i-j),[0:(size_glcm_1-1)]))

p_xminusy((abs(i-j))+1,k) = p_xminusy((abs(i-j))+1,k) +...

glcm(i,j,k);

end

end

end

end

for k = 1:(size_glcm_3)

for i = 1:(2*(size_glcm_1)-1)

out.savgh(k) = out.savgh(k) + (i+1)*p_xplusy(i,k);

out.senth(k) = out.senth(k) - (p_xplusy(i,k)*log(p_xplusy(i,k) + eps));

end

end

for k = 1:(size_glcm_3)

for i = 1:(2*(size_glcm_1)-1)

out.svarh(k) = out.svarh(k) + (((i+1) - out.senth(k))^2)*p_xplusy(i,k);

end

end

for k = 1:size_glcm_3

for i = 0:(size_glcm_1-1)

out.denth(k) = out.denth(k) - (p_xminusy(i+1,k)*log(p_xminusy(i+1,k) + eps));

out.dvarh(k) = out.dvarh(k) + (i^2)*p_xminusy(i+1,k);

end

end

for k = 1:size_glcm_3

hxy(k) = out.entro(k);

for i = 1:size_glcm_1

for j = 1:size_glcm_2

hxy1(k) = hxy1(k) - (glcm(i,j,k)*log(p_x(i,k)*p_y(j,k) + eps));

hxy2(k) = hxy2(k) - (p_x(i,k)*p_y(j,k)*log(p_x(i,k)*p_y(j,k) + eps));

end

hx(k) = hx(k) - (p_x(i,k)*log(p_x(i,k) + eps));

hy(k) = hy(k) - (p_y(i,k)*log(p_y(i,k) + eps));

end

out.inf1h(k) = ( hxy(k) - hxy1(k) ) / ( max([hx(k),hy(k)]) );

out.inf2h(k) = ( 1 - exp( -2*( hxy2(k) - hxy(k) ) ) )^0.5;

end

corm = zeros(size_glcm_3,1);

corp = zeros(size_glcm_3,1);

for k = 1:size_glcm_3

for i = 1:size_glcm_1

for j = 1:size_glcm_2

s_x(k) = s_x(k) + (((i) - u_x(k))^2)*glcm(i,j,k);

s_y(k) = s_y(k) + (((j) - u_y(k))^2)*glcm(i,j,k);

corp(k) = corp(k) + ((i)*(j)*glcm(i,j,k));

corm(k) = corm(k) + (((i) - u_x(k))*((j) - u_y(k))*glcm(i,j,k));

out.cprom(k) = out.cprom(k) + (((i + j - u_x(k) - u_y(k))^4)*...

glcm(i,j,k));

out.cshad(k) = out.cshad(k) + (((i + j - u_x(k) - u_y(k))^3)*...

glcm(i,j,k));

end

end

s_x(k) = s_x(k) ^ 0.5;

s_y(k) = s_y(k) ^ 0.5;

out.autoc(k) = corp(k);

out.corrp(k) = (corp(k) - u_x(k)*u_y(k))/(s_x(k)*s_y(k));

out.corrm(k) = corm(k) / (s_x(k)*s_y(k));

end

function gaborArray = gaborFilterBank(u,v,m,n)

if (nargin ~= 4) % Check correct number of arguments

error('There must be four input arguments (Number of scales and orientations and the 2-D size of the filter)!')

end

%% Create Gabor filters

% Create u*v gabor filters each being an m by n matrix

gaborArray = cell(u,v);

fmax = 0.25;

gama = sqrt(2);

eta = sqrt(2);

for i = 1:u

fu = fmax/((sqrt(2))^(i-1));

alpha = fu/gama;

beta = fu/eta;

for j = 1:v

tetav = ((j-1)/v)*pi;

gFilter = zeros(m,n);

for x = 1:m

for y = 1:n

xprime = (x-((m+1)/2))*cos(tetav)+(y-((n+1)/2))*sin(tetav);

yprime = -(x-((m+1)/2))*sin(tetav)+(y-((n+1)/2))*cos(tetav);

gFilter(x,y) = (fu^2/(pi*gama*eta))*exp(-((alpha^2)*(xprime^2)+(beta^2)*(yprime^2)))*exp(1i*2*pi*fu*xprime);

end

end

gaborArray{i,j} = gFilter;

end

end

%% Show Gabor filters (Please comment this section if not needed!)

% Show magnitudes of Gabor filters:

figure('NumberTitle','Off','Name','Magnitudes of Gabor filters');

for i = 1:u

for j = 1:v

subplot(u,v,(i-1)*v+j);

imshow(abs(gaborArray{i,j}),[]);

end

end

% Show real parts of Gabor filters:

figure('NumberTitle','Off','Name','Real parts of Gabor filters');

for i = 1:u

for j = 1:v

subplot(u,v,(i-1)*v+j);

imshow(real(gaborArray{i,j}),[]);

end

end

function featureVector = gaborFeatures(img,gaborArray,d1,d2)

if (nargin ~= 4)

error('Please use the correct number of input arguments!')

end

if size(img,3) == 3

warning('The input RGB image is converted to grayscale!')

img = rgb2gray(img);

end

img = double(img);

[u,v] = size(gaborArray);

gaborResult = cell(u,v);

for i = 1:u

for j = 1:v

gaborResult{i,j} = imfilter(img, gaborArray{i,j});

end

end

featureVector = [];

for i = 1:u

for j = 1:v

gaborAbs = abs(gaborResult{i,j});

gaborAbs = downsample(gaborAbs,d1);

gaborAbs = downsample(gaborAbs.',d2);

gaborAbs = gaborAbs(:);

gaborAbs = (gaborAbs-mean(gaborAbs))/std(gaborAbs,1);

featureVector = [featureVector; gaborAbs];

end

end

function getData()

%adds an extraction angle per pixel

offsets = [0 1; -1 1;-1 0;-1 -1;2 2];

jpgImagesDir = fullfile('Dataset/Train', '*.jpg');

total = numel( dir(jpgImagesDir) );

jpg_files = dir(jpgImagesDir);

jpg_counter = 0;

%total=length(filename);

gambar={total};

data_feat={total};

stats={total};

data_label={total};

label=1;

limit=5;

j=1;

for i=1:total

%msgbox(jpg_files(j).name)

s=strcat(num2str(i),'.jpg');

file=fullfile('Dataset/Train',s);

%file=fullfile(pathname,filename{i});

gambar{i}=imread(file);

gambar{i}=imresize(gambar{i},[600 600]);

gambar{i}=rgb2gray(gambar{i});

% gambar{i}=imadjust(gambar{i},stretchlim(gambar{i} ));

% gambar{i}=imsharpen(gambar{i},'Radius',1,'Amount',0.5);

glcm=graycomatrix(gambar{i}, 'Offset', offsets, 'Symmetric', true);

stats{i}=graycoprops(glcm);

iglcm=1;

for x=1:5

data_feat{i,x}=stats{i}.Contrast(iglcm);

iglcm=iglcm+1;

end

iglcm=1;

for x=6:10

data_feat{i,x}=stats{i}.Correlation(iglcm);

iglcm=iglcm+1;

end

iglcm=1;

for x=12:16

data_feat{i,x}=stats{i}.Energy(iglcm);

iglcm=iglcm+1;

end

iglcm=1;

for x=18:22

data_feat{i,x}=stats{i}.Homogeneity(iglcm);

iglcm=iglcm+1;

end

data_feat{i,24}=mean2(gambar{i});

data_feat{i,25}=std2(gambar{i});

data_feat{i,26}=entropy(gambar{i});

data_feat{i,27}= mean2(var(double(gambar{i}))); %average image variance

data_feat{i,28}=kurtosis(double(gambar{i}(:)));

data_feat{i,29}=skewness(double(gambar{i}(:)));

%labeling

if i>limit

label=label+1;

data_label{i}=label;

limit=limit+5;

else

data_label{1,i}=label;

end

end

% data is converted to the appropriate data type so that svm is not confused

data_feat=cell2mat(data_feat);

disp(data_feat);

data_label=cell2mat(data_label);

save('data_1.mat','data_feat','data_label');

% --- Executes on button press in pushbutton14.

function pushbutton14_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton14 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

s=get(handles.edit1,'String');

test=imread(s);

test=imresize(test,[600 600]);

test=rgb2gray(test);

offsets = [0 1; -1 1;-1 0;-1 -1;2 2];

glcm=graycomatrix(test, 'Offset', offsets, 'Symmetric', true);

stats=graycoprops(glcm);

data_glcm=struct2array(stats);

iglcm=1;

glcm_contrast={5};

glcm_correlation={5};

glcm_energy={5};

glcm_homogeneity={5};

for x=1:5

glcm_contrast{x}=data_glcm(iglcm);

iglcm=iglcm+1;

end

for x=1:5

glcm_correlation{x}=data_glcm(iglcm);

iglcm=iglcm+1;

end

for x=1:5

glcm_energy{x}=data_glcm(iglcm);

iglcm=iglcm+1;

end

for x=1:5

glcm_homogeneity{x}=data_glcm(iglcm);

iglcm=iglcm+1;

end

rata2=mean2(test);

std_deviation=std2(test);

glcm_entropy=entropy(test);

rata2_variance= mean2(var(double(test)));

glcm_kurtosis=kurtosis(double(test(:)));

glcm_skewness=skewness(double(test(:)));

buat_train=[glcm_contrast(1:5),glcm_correlation(1:5),glcm_energy(1:5),glcm_homogeneity(1:5),rata2,std_deviation,glcm_entropy,rata2_variance,glcm_kurtosis,glcm_skewness];

test_data=cell2mat(buat_train);

disp('GLCM Feature')

disp('Contrast(1) Correlation(2) Energy(3) Homogeneity(4) Mean(5) Standard_Deviation(6) Entropy(7) RMS(8) Variance(9) smoothness(10) Kurtosis(11) Skewness(12) IDM(13)')

input_Feature=test_data

load('data_1.mat');

%load('Test_data.mat');

%disp('Gabor Feature');

load('Gaborfeature.mat');

%Input_Feat=test_data

result = multisvm(data_feat,data_label,test_data);

%result1 = multisvm(data_feat,data_label,data_feat1);

%[Cmat,Accuracy]= confusion_matrix(result1,data_label,{'Desert','Forest','Mountain','Residential','River'});

%[c_matrixp,Result]= confusion.getMatrix(data_label,result1);

%C = confusionmat(data_label,result1)

%disp(result);

if result == 1

A1 = 'actinic keratosis';

set(handles.edit2,'string',A1);

helpdlg('actinic keratosis');

disp('actinic keratosis');

elseif result == 2

A2 = 'Basel cell carcinoma';

set(handles.edit2,'string',A2);

helpdlg('Basel cell carcinoma');

disp('Basel cell carcinoma');

elseif result == 3

A3 = 'cherry nevus';

set(handles.edit2,'string',A3);

helpdlg('cherry nevus');

disp('cherry nevus');

elseif result == 4

A4 = 'dermatofibroma';

set(handles.edit2,'string',A4);

helpdlg('dermatofibroma');

disp('dermatofibroma');

elseif result == 5

A5 = 'Melanocytic nevus';

set(handles.edit2,'string',A5);

helpdlg('Melanocytic nevus');

disp('Melanocytic nevus');

elseif result == 6

A5 = 'Melanoma';

set(handles.edit2,'string',A5);

helpdlg('Melanoma');

disp('Melanoma');

end

% function [itrfin] = multisvm( T,C,test )

%

%

% itrind=size(test,1);

% itrfin=[];

% Cb=C;

% Tb=T;

% itr=1;

%

% for tempind=1:itrind

% tst=test(tempind,:);

% C=Cb;

% T=Tb;

% u=unique(C);

% N=length(u);

% c4=[];

% c3=[];

% j=1;

% k=1;

% if(N>2)

% itr=1;

% classes=0;

% cond=max(C)-min(C);

% while((classes~=1)&&(itr<=length(u))&& size(C,2)>1 && cond>0)

% %This while loop is the multiclass SVM Trick

% c1=(C==u(itr));

% newClass=c1;

% svmStruct = svmtrain(T,newClass,'kernel_function','rbf'); % I am using rbf kernel function, you must change it also

% classes = svmclassify(svmStruct,tst);

%

% % This is the loop for Reduction of Training Set

% for i=1:size(newClass,2)

% if newClass(1,i)==0;

% c3(k,:)=T(i,:);

% k=k+1;

% end

% end

% T=c3;

% c3=[];

% k=1;

%

% % This is the loop for reduction of group

% for i=1:size(newClass,2)

% if newClass(1,i)==0;

% c4(1,j)=C(1,i);

% j=j+1;

% end

% end

% C=c4;

% c4=[];

% j=1;

%

% cond=max(C)-min(C); % Condition for avoiding group

% %to contain similar type of values

% %and the reduce them to process

%

% % This condition can select the particular value of iteration

% % base on classes

% if classes~=1

% itr=itr+1;

% end

% end

% end

%

% valt=Cb==u(itr); % This logic is used to allow classification

% val=Cb(valt==1); % of multiple rows testing matrix

% val=unique(val);

% itrfin(tempind,:)=val;

% end

% Give more suggestions for improving the program.

function [result] = multisvm(TrainingSet,GroupTrain,TestSet)

u=unique(GroupTrain);

numClasses=length(u);

result = zeros(length(TestSet(:,1)),1);

%build models

for k=1:numClasses

%Vectorized statement that binarizes Group

%where 1 is the current class and 0 is all other classes

G1vAll=(GroupTrain==u(k));

models(k) = svmtrain(TrainingSet,G1vAll);

end

%classify test cases

for j=1:size(TestSet,1)

for k=1:numClasses

if(svmclassify(models(k),TestSet(j,:)))

break;

end

end

result(j) = k;

disp('Result')

disp(result(j));

end

function exCode1()

folder = 'Dataset';

dirImage = dir( folder );

numData = size(dirImage,1);

M ={} ;

for i=1:numData

nama = dirImage(i).name;

if regexp(nama, '(D|F)-[0-9]{1,2}.jpg')

B = cell(1,2);

if regexp(nama, 'D-[0-9]{1,2}.jpg')

B{1,1} = double(imread([folder, '/', nama]));

B{1,2} = 1;

elseif regexp(nama, 'F-[0-9]{1,2}.jpg')

B{1,1} = double(imread([folder, '/', nama]));

B{1,2} = -1;

end

M = cat(1,M,B);

end

end

numDataTrain = size(M,1);

class = zeros(numDataTrain,1);

arrayImage = zeros(numDataTrain, 300 * 300);

for i=1:numDataTrain

im = M{i,1} ;

im = rgb2gray(im);

im = imresize(im, [300 300]);

im = reshape(im', 1, 300*300);

arrayImage(i,:) = im;

class(i) = M{i,2};

end

SVMStruct = svmtrain(arrayImage, class);

imTest = double(imread(s));

imTest = rgb2gray(imTest);

imTest = imresize(imTest, [300 300]);

imTest = reshape(imTest',1, 300*300);

result = svmclassify(SVMStruct, imTest);

if(result==1)

msgbox('Desert');

else

msgbox('Forest');

end

function exCode2()

folder = 'Dataset';

dirImage = dir( folder );

numData = size(dirImage,1);

M ={} ;

for i=1:numData

nama = dirImage(i).name;

if regexp(nama, '(M|R)-[0-9]{1,2}.jpg')

B = cell(1,2);

if regexp(nama, 'M-[0-9]{1,2}.jpg')

B{1,1} = double(imread([folder, '/', nama]));

B{1,2} = 1;

elseif regexp(nama, 'R-[0-9]{1,2}.jpg')

B{1,1} = double(imread([folder, '/', nama]));

B{1,2} = -1;

end

M = cat(1,M,B);

end

end

numDataTrain = size(M,1);

class = zeros(numDataTrain,1);

arrayImage = zeros(numDataTrain, 300 * 300);

for i=1:numDataTrain

im = M{i,1} ;

im = rgb2gray(im);

im = imresize(im, [300 300]);

im = reshape(im', 1, 300*300);

arrayImage(i,:) = im;

class(i) = M{i,2};

end

SVMStruct = svmtrain(arrayImage, class);

imTest = double(imread(s));

imTest = rgb2gray(imTest);

imTest = imresize(imTest, [300 300]);

imTest = reshape(imTest',1, 300*300);

result = svmclassify(SVMStruct, imTest);

if(result==1)

msgbox('Mountain');

else

msgbox('Residential');

end

function exCode3()

folder = 'Dataset';

dirImage = dir( folder );

numData = size(dirImage,1);

M ={} ;

for i=1:numData

nama = dirImage(i).name;

if regexp(nama, '(RI|D)-[0-9]{1,2}.jpg')

B = cell(1,2);

if regexp(nama, 'RI-[0-9]{1,2}.jpg')

B{1,1} = double(imread([folder, '/', nama]));

B{1,2} = 1;

elseif regexp(nama, 'D-[0-9]{1,2}.jpg')

B{1,1} = double(imread([folder, '/', nama]));

B{1,2} = -1;

end

M = cat(1,M,B);

end

end

numDataTrain = size(M,1);

class = zeros(numDataTrain,1);

arrayImage = zeros(numDataTrain, 300 * 300);

for i=1:numDataTrain

im = M{i,1} ;

im = rgb2gray(im);

im = imresize(im, [300 300]);

im = reshape(im', 1, 300*300);

arrayImage(i,:) = im;

class(i) = M{i,2};

end

SVMStruct = svmtrain(arrayImage, class);

imTest = double(imread(s));

imTest = rgb2gray(imTest);

imTest = imresize(imTest, [300 300]);

imTest = reshape(imTest',1, 300*300);

result = svmclassify(SVMStruct, imTest);

if(result==1)

msgbox('River');

else

msgbox('Desert');

end

function edit2_Callback(hObject, eventdata, handles)

% hObject handle to edit2 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of edit2 as text

% str2double(get(hObject,'String')) returns contents of edit2 as a double

% --- Executes during object creation, after setting all properties.

function edit2_CreateFcn(hObject, eventdata, handles)

% hObject handle to edit2 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))

set(hObject,'BackgroundColor','white');

end

% --- Executes on button press in pushbutton15.

function pushbutton15_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton15 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% --- Executes on button press in pushbutton16.

function pushbutton16_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton16 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

str=get(handles.edit1,'String');

I1 = imread(str);

I4 = imadjust(I1,stretchlim(I1));

I5 = imresize(I4,[300,400]);

figure

imshow(I5);title(' Processed Image ');

set(handles.pushbutton1,'enable','off');

set(handles.pushbutton16,'enable','off');

set(handles.pushbutton12,'enable','on');

% --- Executes on button press in pushbutton17.

function pushbutton17_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton17 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)