I am getting ''Error using classreg.l​earning.Fu​llClassifi​cationRegr​essionMode​l.prepareD​ataCR '' error in diabetic retinopahty classification code

% Diyabetik Retinopati Sınıflandırma Kodu

% 1. Dosyaları oku ve veriyi yükle

healthy_path = 'Healthy\'; % Healthy dosyasının bulunduğu dizin

mild_DR_path = 'Mild DR\'; % Mild DR dosyasının bulunduğu dizin

moderate_DR_path = 'Moderate DR\'; % Moderate DR dosyasının bulunduğu dizin

proliferate_DR_path = 'Proliferate DR\'; % Proliferate DR dosyasının bulunduğu dizin

severe_DR_path = 'Severe DR\'; % Severe DR dosyasının bulunduğu dizin

healthy_images = imageDatastore(healthy_path);

mild_DR_images = imageDatastore(mild_DR_path);

moderate_DR_images = imageDatastore(moderate_DR_path);

proliferate_DR_images = imageDatastore(proliferate_DR_path);

severe_DR_images = imageDatastore(severe_DR_path);

% 2. Veriyi görselleştir

figure;

subplot(2,3,1);

imshow(readimage(healthy_images,1));

title('Healthy');

subplot(2,3,2);

imshow(readimage(mild_DR_images,1));

title('Mild DR');

subplot(2,3,3);

imshow(readimage(moderate_DR_images,1));

title('Moderate DR');

subplot(2,3,4);

imshow(readimage(proliferate_DR_images,1));

title('Poliferate DR');

subplot(2,3,5);

imshow(readimage(severe_DR_images,1));

title('Severe DR');

% 3. VGG-16 modelini yükle

net = vgg16;

% 4. CNN özellik çıkarıcılarını kullanarak eğitim verisini önceden işle

healthy_features = activations(net, healthy_images, 'fc7');

mild_DR_features = activations(net, mild_DR_images, 'fc7');

moderate_DR_features = activations(net, moderate_DR_images, 'fc7');

proliferate_DR_features = activations(net, proliferate_DR_images, 'fc7');

severe_DR_features = activations(net, severe_DR_images, 'fc7');

% Sınıf özellik vektörlerinin boyutlarını kontrol et

disp('Size of healthy_features:');

disp(size(healthy_features));

disp('Size of mild_DR_features:');

disp(size(mild_DR_features));

disp('Size of moderate_DR_features:');

disp(size(moderate_DR_features));

disp('Size of proliferate_DR_features:');

disp(size(proliferate_DR_features));

disp('Size of severe_DR_features:');

disp(size(severe_DR_features));

% Tüm sınıf özellik vektörlerini aynı boyuta getir

min_size = min([size(healthy_features, 1), size(mild_DR_features, 1), size(moderate_DR_features, 1), size(proliferate_DR_features, 1), size(severe_DR_features, 1)]);

healthy_features = healthy_features(1:min_size, :);

mild_DR_features = mild_DR_features(1:min_size, :);

moderate_DR_features = moderate_DR_features(1:min_size, :);

proliferate_DR_features = proliferate_DR_features(1:min_size, :);

severe_DR_features = severe_DR_features(1:min_size, :);

% Yeniden boyutlandırılmış özellik vektörlerini birleştir

X_train = [healthy_features, mild_DR_features, moderate_DR_features, proliferate_DR_features, severe_DR_features];

% Etiketleri oluştur

Y_train = [ones(min_size, 1),2*ones(min_size, 1),3*ones(min_size, 1), 4*ones(min_size, 1), 5*ones(min_size, 1)];

% 6. Modeli eğit

svm_model = fitcecoc(X_train, Y_train); % here i get the error specified below

dataset in here

Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 234)

X and Y do not have the same number of observations.

Error in classreg.learning.classif.FullClassificationModel.prepareData (line 827)

classreg.learning.FullClassificationRegressionModel.prepareDataCR(...

Error in classreg.learning.FitTemplate/fit (line 246)

this.PrepareData(X,Y,this.BaseFitObjectArgs{:});

Error in ClassificationECOC.fit (line 116)

this = fit(temp,X,Y);

Error in fitcecoc (line 357)

obj = ClassificationECOC.fit(X,Y,varargin{:});

% 7. Test verisini oluştur

test_path = 'path/to/test'; % Test dosyasının bulunduğu dizin

test_images = imageDatastore(test_path);

% 8. Test verisini önceden işle

test_features = activations(net, test_images, 'fc7');

% 9. Modeli kullanarak sınıflandırma yap

Y_pred = predict(svm_model, test_features);

% 10. Sonuçları görselleştir

figure;

subplot(1,2,1);

imshow(readimage(test_images,1));

title(['True Label: ' num2str(Y_test(1))]);

subplot(1,2,2);

imshow(readimage(test_images,2));

title(['Predicted Label: ' num2str(Y_pred(1))]);

% 11. Modelin başarı oranını değerlendir

Y_test = test_images.Labels; % Gerçek etiketleri al

accuracy = sum(Y_pred == Y_test) / numel(Y_test);

disp(['Accuracy: ' num2str(accuracy)]);

Answer 1

[Malay Agarwal]

Hi @Senanur Özdemir,

I understand that you're trying to use VGG-16 as a feature extractor for your images and then use an SVM classifier to classify those images.

Reading the error message, it says X and Y do not have the same number of observations. To use the fitcecoc function, the features and the labels should have the same number of observations. If there are m images, then X should be , where n is the number of features, and Y should be .

In the code above, X (X_train) is a row vector and Y (Y_train) is a row vector. Please consider the explanation below to understand why this is so.

Executing the following code:

healthy_features = activations(net, healthy_images, 'fc7');

The output is since the fc7 layer in VGG-16 has output units and there are healthy images. The first two dimensions are supposed to represent the height and width respectively but since this is a fully-connected layer, the height and width are 1. The other feature vectors have similar dimensions and differ only in the last dimension since there are different number of images. This behavior of the activations function is documented here: https://www.mathworks.com/help/deeplearning/ref/seriesnetwork.activations.html#mw_b65e2845-af4a-42ab-ae94-cbd612c5324b.

Now, the following code:

min_size = min([size(healthy_features, 1), size(mild_DR_features, 1), size(moderate_DR_features, 1), size(proliferate_DR_features, 1), size(severe_DR_features, 1)]);

Sets min_size to 1 since the first dimension of each feature vector is 1. Then, the code:

healthy_features = healthy_features(1:min_size, :);

Flattens the feature vector to (). Similarly, the other feature vectors are flattened. The code:

X_train = [healthy_features, mild_DR_features, moderate_DR_features, proliferate_DR_features, severe_DR_features];

Concatenates all the flattened vectors into a single row vector, resulting in the row vector. Also, since min_size is 1, the code:

Y_train = [ones(min_size, 1),2*ones(min_size, 1),3*ones(min_size, 1), 4*ones(min_size, 1), 5*ones(min_size, 1)];

Creates a simple vector with the value [1,2,3,4,5], resulting in the row vector.

Manually creating a datastore for each label, extracting the features and concatenating them can be avoided by:

• Using a single datastore for the dataset by specifying the IncludeSubfolders option and setting LabelSource to foldernames when creating the datastore. This will ensure that images from each subfolder are part of the datastore and that each image is assigned a label according to the subfolder it is in. This will also allow you to use the Labels property to create Y_train.
• Setting the OutputAs option to rows to ensure that the activations function outputs the correct dimension. This will ensure that the output from the function is .

Assuming that the root data folder is colored_images/, the code below should fix the issue (I am omitting the plotting and evaluation code for brevity):

imds = imageDatastore("colored_images\", "IncludeSubfolders", true, "LabelSource", "foldernames");
% Plotting code
net = vgg16;
X_train = activations(net, imds, "fc7", "OutputAs", "rows");
Y_train = imds.Labels;
svm_model = fitcecoc(X_train, Y_train);
% Evaludation code

Hope this helps!