How to read a single image from a folder and then classify it with a trained neural network?

I trained my network for skin tumor classification and then saved it. Now I want to test the accuracy for some new images. It works fine using imageDatastore but what I really want is to be able to browse in my computer for an image and classify it using the trained network. Is this possible?

Below I have attached the test code for a set of images.

load incercare.mat
imds= imageDatastore('E:/LICENTA/BD_Skin_Cancer_Screening/mini_data/test/', ...
    'IncludeSubfolders', true, ...
    'LabelSource', 'foldernames' );
[YPred,probs] = classify(netTransfer,imds);
accuracy = mean(YPred == imds.Labels)
for i=1:10
     subplot(2,5,i)
 I = readimage(imds,idx(i));
    imshow(I)
    label = YPred(idx(i));
    title(string(label) + ", " + num2str(100*max(probs(idx(i),:)),3) + "%");
end

[Amit]

Dear Malina,

Accoridng to me Backpropagation Feedforward neural network would be best suited for your purpose.

Please send me your 'incercare.mat' file by email on [e-mail], I will analyze this file for its variables and suggest you best suited approach to get your work done..

Answer 1

[Prateek Rai]

To my understanding, you trained the network for skin tumor classification and want to classify a single image using the trained network.

A possible workaround is:

• Step 1: Read the image.

image = imread(xyz) % xyz is the location of the image in computer.

• Step 2: You can use use the nework variable and feed the image as its argument to compute the network output.

classificationOutput = netTransfer(image);

You can also refer to Classify Patterns with a Neural Network MathWorks documentation page to find more on classification with a neural network.