Error using == Arrays have incompatible sizes for this operation. How solve this error??

clc;
clear all;
close all;
% Define the main directories
mainDirectories = {'Training', 'Testing'};
% Define the classes
classes = {'Glioma Tumor', 'Meningiloma Tumor','No Tumor', 'Pituitary Tumor'};
for mainIndex = 1:numel(mainDirectories)
    mainDir = mainDirectories{mainIndex};
    
    % Set the directory containing subdirectories for each class
    datasetDirectory = fullfile('C:\Users\sukla\OneDrive\Desktop\Dataset', mainDir);
    % List only directories in the specified directory
    subDirectories = dir(datasetDirectory);
    subDirectoryNames = {subDirectories([subDirectories.isdir]).name};
    subDirectoryNames = subDirectoryNames(~ismember(subDirectoryNames, {'.', '..'}));
    % Display the names of subdirectories
    disp(subDirectoryNames);
    df = [];
    GroupTrain = cell(0, 1);
    max_area = [];
    
    % Loop through subdirectories (each corresponds to a class)
    for classIndex = 1:numel(subDirectoryNames)
        classDir = subDirectoryNames{classIndex};
    %Skip entries for the current directory (.) and parent directory (..)
    if strcmp(classDir, '.') || strcmp(classDir, '..')
        continue;
    end
        classPath = fullfile(datasetDirectory, classDir);
        % Display the current class directory
    disp(['Class Directory: ' classDir]);
    % Load images for the current class
    imageFiles = dir(fullfile(classPath, '*.jpg'));
    
for i = 1:numel(imageFiles)
    % Load the image
    x = imread(fullfile(classPath, imageFiles(i).name));
   
   %%%%%%%texture features%%%%%%%%
   y=rgb2gray(x);
   
    % Define the standard deviation (sigma) and filter size
    sigma = 1.5;
    filter_size = 5; % Choose an appropriate filter size based on sigma
    
    % Create a 2D Gaussian filter
    gaussian_filter = fspecial('gaussian', [filter_size, filter_size], sigma);
    
    % Apply the Gaussian filter to the image
    smoothed_image = imfilter(y, gaussian_filter, 'conv', 'replicate');
    % %% Global Thresholding
    global_threshold = 160; % Adjust threshold value
    binaryImage = smoothed_image > global_threshold;
    %% Define a structuring element (disk-shaped in this case)
    se = strel('disk', 5);
    %% Perform dilation
    dilatedImage = imdilate(binaryImage, se);
%% Morphological Operation
label = bwlabel(dilatedImage);
stats = regionprops(logical(dilatedImage), 'Solidity', 'Area', 'BoundingBox');
density = [stats.Solidity];
area = [stats.Area];
high_dense_area = density > 0.6;
max_area = max(area(high_dense_area));
disp(max_area);
disp(area);
tumor_label = find(area == max_area);
tumor = ismember(label, tumor_label);
if max_area > 200
    figure;
    imshow(tumor)
    title('Tumor alone', 'FontSize', 20);
else
    h = msgbox('No Tumor!!', 'status');
    return;
end
%% Bounding box
box = stats(tumor_label);
wantedBox = box.BoundingBox;
% figure
% imshow(x);
% title('Bounding Box', 'FontSize', 20);
% hold on;
% rectangle('Position', wantedBox, 'EdgeColor', 'y');
% hold off;
% Debug output
disp(['Size of label: ' num2str(size(label))]);
disp(['Size of stats: ' num2str(size(stats))]);
disp(['Size of area: ' num2str(size(area))]);
disp(['Size of max_area: ' num2str(size(max_area))]);
disp(['Size of tumor_label: ' num2str(size(tumor_label))]);
%% Getting Tumor Outline - image filling, eroding, subtracting
% erosion the walls by a few pixels
dilationAmount = 5;
rad = floor(dilationAmount);
[r,c] = size(tumor);
filledImage = imfill(tumor, 'holes');
for i=1:r
   for j=1:c
       x1=i-rad;
       x2=i+rad;
       y1=j-rad;
       y2=j+rad;
       if x1<1
           x1=1;
       end
       if x2>r
           x2=r;
       end
       if y1<1
           y1=1;
       end
       if y2>c
           y2=c;
       end
       erodedImage(i,j) = min(min(filledImage(x1:x2,y1:y2)));
   end
end
%% subtracting eroded image from original BW image
% Resize erodedImage to match the dimensions of tumorOutline
 tumorOutline=tumor;
 erodedImage = imresize(erodedImage, size(tumorOutline));
% Subtract erodedImage from tumorOutline
 tumorOutline = tumorOutline & ~erodedImage;
 tumorOutline(erodedImage)=0;
   lbpB1 = extractLBPFeatures(erodedImage,'Upright',false);
   lb1=sum(lbpB1);
   glcm=graycomatrix(y,'Offset',[2,0;0,2]);   %Gray-Level Co-Occurrence Matrix 
   st1=graycoprops(glcm,{'contrast','homogeneity'});
   st2=graycoprops(glcm,{'correlation','energy'});
   
   f1=st1.Contrast;
   f2=st1.Homogeneity;
   f3=st2.Correlation;
   f4=st2.Energy;
   
   Fr=horzcat([lb1,f1,f2,f3,f4]);
   
   df=[df;Fr];
  
    %% Inserting the outline in the filtered image in green color
    rgb = cat(3, y, y, y);
    red = rgb(:,:,1);
    green = rgb(:,:,2);
    blue = rgb(:,:,3);
    red(tumorOutline) = 255;
    green(tumorOutline) = 0;
    blue(tumorOutline) = 0;
    tumorOutlineInserted = cat(3, red, green, blue);
    % Store the class label for this image
    GroupTrain = [GroupTrain; classDir];
end
    end  
end
  
 % Define the classes
classes = {'No Tumor', 'Glioma Tumor', 'Meningiloma Tumor', 'Pituitary Tumor'}; % Replace with your class labels
% Initialize an empty cell array to store trained SVM models
SVMModels = cell(numel(classes), 1);
% Train an SVM model for each class
for j = 1:numel(classes)
    % Extract features and labels for the current class
    idx = strcmp(GroupTrain, classes{j});
    SVMModels{j} = fitcsvm(df, idx, 'ClassNames', [false true], 'Standardize', true, 'KernelFunction', 'rbf', 'BoxConstraint', 1);
end

Error using ClassificationSVM.prepareData
No class names are found in input labels.

Error in classreg.learning.FitTemplate/fit (line 247)
this.PrepareData(X,Y,this.BaseFitObjectArgs{:});

Error in ClassificationSVM.fit (line 261)
this = fit(temp,X,Y);

Error in fitcsvm (line 342)
obj = ClassificationSVM.fit(X,Y,RemainingArgs{:});

% Test your image and classify it
% Load and process your test image
%cd ..
%%%%%%%%%%%%get test image %%%%%%%%%%
    [f,p]=uigetfile('*.*');
    test=imread(strcat(p,f));
   
   %%%%%%%texture features test image%%%%%%%%
   y=rgb2gray(test);
    % Define the standard deviation (sigma) and filter size
    sigma = 1.5;
    filter_size = 5; % Choose an appropriate filter size based on sigma
    
    % Create a 2D Gaussian filter
    gaussian_filter = fspecial('gaussian', [filter_size, filter_size], sigma);
    
    % Apply the Gaussian filter to the image
    smoothed_image = imfilter(y, gaussian_filter, 'conv', 'replicate');
    
    %% Global Thresholding
     global_threshold = 160; % Adjust threshold value
     binaryImage = smoothed_image > global_threshold;
    figure
    imshow(binaryImage);
    %% Define a structuring element (disk-shaped in this case)
    se = strel('disk', 5);
    %% Perform dilation
    dilatedImage = imdilate(binaryImage, se);
    %% Morphological Operation
    label = bwlabel(dilatedImage);
    stats = regionprops(logical(dilatedImage), 'Solidity', 'Area', 'BoundingBox');
    density = [stats.Solidity];
    area = [stats.Area];
    high_dense_area = density > 0.6;
    max_area = max(area(high_dense_area));
    tumor_label = find(area == max_area);
    tumor = ismember(label, tumor_label);
    if max_area > 200
        figure;
        imshow(tumor)
        title('Tumor alone', 'FontSize', 20);
    else
        h = msgbox('No Tumor!!', 'status');
        return;
    end
    %% Bounding box
    box = stats(tumor_label);
    wantedBox = box.BoundingBox;
    figure
    imshow(y);
    title('Bounding Box', 'FontSize', 20);
    hold on;
    rectangle('Position', wantedBox, 'EdgeColor', 'y');
    hold off;
    %% Getting Tumor Outline - image filling, eroding, subtracting
    % erosion the walls by a few pixels
    dilationAmount = 5;
    rad = floor(dilationAmount);
    [r,c] = size(tumor);
    filledImage = imfill(tumor, 'holes');
    for i=1:r
       for j=1:c
           x1=i-rad;
           x2=i+rad;
           y1=j-rad;
           y2=j+rad;
           if x1<1
               x1=1;
           end
           if x2>r
               x2=r;
           end
           if y1<1
               y1=1;
           end
           if y2>c
               y2=c;
           end
           erodedImage(i,j) = min(min(filledImage(x1:x2,y1:y2)));
       end
    end
    figure
    imshow(erodedImage);
    title('Eroded Image','FontSize',20);
% 
% %% subtracting eroded image from original BW image
% tumorOutline=tumor;
% tumorOutline(erodedImage)=0;
%% subtracting eroded image from original BW image
% Resize erodedImage to match the dimensions of tumorOutline
 tumorOutline=tumor;
 erodedImage = imresize(erodedImage, size(tumorOutline));
% Subtract erodedImage from tumorOutline
 tumorOutline = tumorOutline & ~erodedImage;
 tumorOutline(erodedImage)=0;
figure;  
imshow(tumorOutline);
title('Tumor Outline','FontSize',20);
   lbpB1 = extractLBPFeatures(erodedImage,'Upright',false);
   lb1=sum(lbpB1);
   glcm=graycomatrix(y,'Offset',[2,0;0,2]);   %Gray-Level Co-Occurrence Matrix 
   st1=graycoprops(glcm,{'contrast','homogeneity'});
   st2=graycoprops(glcm,{'correlation','energy'});
   
   f1=st1.Contrast;
   f2=st1.Homogeneity;
   f3=st2.Correlation;
   f4=st2.Energy;
  
   Testftr=horzcat([lb1,f1,f2,f3,f4]);
  
    %% Inserting the outline in the filtered image in green color
    rgb = cat(3, y, y, y);
    red = rgb(:,:,1);
    green = rgb(:,:,2);
    blue = rgb(:,:,3);
    red(tumorOutline) = 255;
    green(tumorOutline) = 0;
    blue(tumorOutline) = 0;
    tumorOutlineInserted = cat(3, red, green, blue);
    figure
    imshow(tumorOutlineInserted);
    title('Detected Tumer','FontSize',20);
    
% Loop through the trained models for all classes to classify your test image
Scores = zeros(size(Testftr, 1), numel(classes)); % Assuming 'Testftr' contains features of the test image
for j = 1:numel(classes)
    [~, score] = predict(SVMModels{j}, Testftr); % Use 'Testftr' for the test image
    Scores(:, j) = score(:, 2);
end
% Determine the predicted class with the highest score
[~, maxScore] = max(Scores, [], 2);
predictedClass = classes(maxScore);
% Display the result message (as you have done in your code)
if strcmp(predictedClass, 'Glioma Tumor')
    msgbox('Glioma Tumor');
elseif strcmp(predictedClass, 'Meningiloma Tumor')
    msgbox('Meningiloma Tumor');
elseif strcmp(predictedClass, 'No Tumor')
    msgbox('No Tumor');
elseif strcmp(predictedClass, 'Pituitary Tumor')
    msgbox('Pituitary Tumor');
else
    msgbox('None');
end

{'glioma_tumor'} {'meningioma_tumor'} {'no_tumor'} {'pituitary_tumor'}

Class Directory: glioma_tumor

1316

Columns 1 through 8

158 92 390 1316 1255 187 402 78

Column 9

69

Size of label: 512 512

Size of stats: 9 1

Size of area: 1 9

Size of max_area: 1 1

Size of tumor_label: 1 1

1316

4938 5721

Error using ==

Arrays have incompatible sizes for this operation.

Error in imtest (line 93)

tumor_label = find(area == max_area);

Answer 1

[Jon]

The error is telling you that the dimensions of your variable named, area are not equal to the dimensions of the variable called max_area. To use "==" both variables must have the same dimensions, or one of them must be a scalar (only one element).

Check the dimensions of the two variables just before the statement that throws the error. If they are not what you think then work backwards to see where you assigned them, and get them to be compatible.

[Dyuman Joshi]

"To use "==" both variables must have the same dimensions, or one of them must be a scalar (only one element)."

x=(1:10);
x==x'

ans = 10×10 logical array
   1   0   0   0   0   0   0   0   0   0
   0   1   0   0   0   0   0   0   0   0
   0   0   1   0   0   0   0   0   0   0
   0   0   0   1   0   0   0   0   0   0
   0   0   0   0   1   0   0   0   0   0
   0   0   0   0   0   1   0   0   0   0
   0   0   0   0   0   0   1   0   0   0
   0   0   0   0   0   0   0   1   0   0
   0   0   0   0   0   0   0   0   1   0
   0   0   0   0   0   0   0   0   0   1

[Jon]

So apparently you can also get some valid results due to MATLAB applying implicit expansion. While if I think it through, I can understand how MATLAB computes x == y with x a row and y a column with the same number of elements as y, the results are not obvious.

Implicit expansion can provide a powerful shorthand for expressing certain operations. It can also easily lead to surprising behavior, and obscure errors when the user unintentionally operates on two vectors with the same number of elements, but where one is a row and the other is a column.