CODEWITHSUNDEEP
svmfeature-extraction
Christian Salvatore
Christian Salvatore

Reputation: 11

SVM: Adding Clinical Features To Feature Vector Extracted From Image

I'm using SVM to classify clinical images of patients belonging to two different groups (patients vs. controls). I use PCA to extract a vector of features from each image, but I'd like to add other clinical information (for example, the output value of a clinical exam) in order to include it in the classification process. Is there a way to do this? I didn't find exhaustive suggestions in literature. Thanks in advance.

Upvotes: 1

Views: 136

Answers (2)

Valentin H
Valentin H

Reputation: 7458

The question is pretty old, I' post my answer though.

If you have to scale your values, make sure that the new values are scaled to the similar range of your values in PCA-vector. If your PCA vectors of features have constant length, you just start enumerating your features from length+1 e.g. for SVM input (libsvm):

1 1:<PCAval1> ... N:<PCAvalN> N+1:<Clinical exam value 1> ...

I've made a test adding such general features for cell recognition and the accuracy raised.

This Guide describes how to use enumerator-features.

P.S.: In my test I've isolated, and squeezed cells from microscope image to a matrix 16x16. Each pixel in this matrix was a feature - 256 features. Additionally I've added some features as original size, moments, etc.

Upvotes: 0

Pedrom
Pedrom

Reputation: 3823

You could just append the new information at the end of each sample. Other approach that you could try is having two additional classifiers, one that you could train with the additional information and a third classifier that would take the output of the other two classifiers as input to product a final prediction.

Upvotes: 1

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