what would be the right feature extraction methods for these images?
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I am trying to classify cataract images, First i crop out the pupil area and save it in another folder, then i execute the wavelet transform on these cropped images using wavedec function and sym8 filter, then i take the approximation coefficients as the feature vector and the final step would be sending these feature vectors to neural network, after trying the neural network to classify my data set which contains 51 images,I have tried 10,20, 50, 70 hidden layers,But It shows that only 90% is the percentage of the correct classified images. so I want to increase this percentage,
- So, any suggestion to use something other than wavelet?
- why when i increased the dataset size to 83 images the neural network showed me bad results such as 70 or 60%?!
- should i stop using Neural and start looking for another classifier?
6 Comments
Greg Heath
on 21 Oct 2016
I assume you meant MATRIX instead of VECTOR. Even then, the matrix sizes for N pairs of I-dimensional "I"nput vectors and corresponding O-dimensional "O"utput target vectors are
[ I N ] = size(input)
[ O N ] = size(target)
For a 2-class classifier, O = 2, therefore I do not understand the value 5.
Using a NN input vector of length 8600 makes no sense at all.
Please explain your numbers w.r.t. a NN classifier with I-H-O node topology.
Confused,
Greg
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