Neural network classifier for classification of brain MRI Images

I have a data set of total 35 images, 20 used for training and rest 15 for testing. Two class labels normal and abnormal. Total ten texture features namely energy, entropy, contrast, cluster shade, covariance, inertia, cluster prominance, cluster shade, idm, angular second moment are extracted. How to use neural network for this classification purpose.

Answer 1

[Greg Heath]

It doesn't sound like you have a sufficient amount of data for precision estimation.

Therefore I would recommend many repetitions of crossvalidation using patternnet. Combining validation stopping and regularization might be worthwhile.

 [ I N ] = size(input) % [ 10 35 ]
 [ O N ] = size(target) % [ 1 35 ]

With no data division, the number of unknown weights will exceed the number of training equations when the number of hidden nodes exceeds the upper bound

 Hub = -1 + ceil( (N*O-O)/ (I+O+1) ) = 2

Therefore you may have to experiment more than you would like to with input variable reduction and regularization.

Hope this helps.

Greg