When I add new image , I want to get "the result" of image in neural network pattern recognition. How can I do this according to codes added?

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I am studying about mammogram images to detect cancer on breast image. There are three situations benign, malign and normal.
I trained my neural netwok with 100 images and get result 95% . I will create user interface and will select an image and get the result of what is this image benign or malign or normal .
I mean my training is done and I want to see my neural network ability to detect result. I will upload a new image in user interface and want to see result of image benign or malign or normal . But I don't know which codes can I add in neural network?
Here is code neural pattern recognition created by matlab ownself. Which code should be added to detect new image (for example for 101 th image) ?
% Solve a Pattern Recognition Problem with a Neural Network
% Script generated by Neural Pattern Recognition app
% Created 07-Feb-2021 15:50:44
%
% This script assumes these variables are defined:
%
% x - input data.
% y - target data.
x = x;
t = y;
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainscg'; % Scaled conjugate gradient backpropagation.
% Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize, trainFcn);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,x,t);
% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotconfusion(t,y)
%figure, plotroc(t,y)

回答(1 个)

Abhishek Gupta
Abhishek Gupta 2021-2-17
编辑:Abhishek Gupta 2021-2-17
Hi,
As per my understanding, you want to make predictions for new input using your trained network. You can do the same using the 'predict()' function in MATLAB: -
predictions = predict(trainedNetwork,newImages);
For more information, check out the documentation here: -
  4 个评论
Ali Zulfikaroglu
Ali Zulfikaroglu 2021-2-20
I used glcm feature extraction methods to detect properties of image and glcm gives 88 properties (such as homogenity, entropy, energy...). I wrote 88 numbers in excel file and these are my input and I write output according to this 88 properties . I have data in excel file . In excel file , there is 88x100 input and 2x100 target. The code above x= input and y= my target data. I trained my network with this inputs and targets . You can see code above. And then I added new image and get glcm properties of it again 88 features . And I wonder its output "what" according to this 88 features .
predictions = predict(net,a);
net is my trained network and a is that new image's 88 properties.
and this code doen't work.
But I used new_outputs=sim(net,a) it worked.
Should we use "sim" command or "predict" command ? "sim" command is true in here?
Abhishek Gupta
Abhishek Gupta 2021-2-22
What error are you getting while using "predict()"? What is the dimension of 'a'? Note that "a" should be an N-by-numFeatures numeric array, where N is the number of observations and numFeatures is the number of features of the input data. I see N=1 in your case, so "a" should be of (1x88) dimension.
For more information, check out the "Input Arguments" section of the documentation: -

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