MATLAB: How to test an input (after training) by using Neural Network

Question

Hello,

I want to do cancer detection using NN. In the training scheme: 1) Feature_vectors [5,10000] (Five attributes by number of columns based on number of images) 2) target vectors [2,10000] (cancer 1, not cancer 0 – corresponds to relevant features) 3) [net,tr]= train(net,feature_vectors,target_vectors); By doing so, I obtain the net which is essential for testing section.

Now, I want to present an test input image and expect to get an result whether it is possibly cancer image or not cancer: 1) Y = sim(net,feature_vectors); feature_vectors:extracted features from test image and net comes from training.

Is this the correct methodology to test using neural network ? Or How should I do testing part ? How can I get percent rate of cancer results for a test image ? Can I create a confusion matrix likewise in training part ? (But we don't know the target vectors in testing part)

Would you please help me out how to correctly perform test using Neural Network in Matlab ?

Thank you,

Sertan,

Answer 1

You cannot "test" a net without a known input/target pair.

That is why trn/val/tst data division is a default. The divided test subset is in no way involved in the design process. Therefore it yields an UNBIASED estimate of performance on unseen data.

If the data division is random, you can repeat the design with a different division and obtain another UNBIASED estimate.

Because the initial weights are also random, not all designs will be successful. However you can obtain reasonably accurate summary statistics from a sufficient number of multiple designs.

Examples of multiple designs obtained via a double for loop over(outer loop) number of hidden nodes and (inner loop) random initial weights and data divisions can be found by searching on

 greg Hmin:dH:Hmax Ntrials

Hope this helps.

Thank you for formally accepting my answer

Greg