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I'm a computer engineering student and I'm about to work on my master thesis. My professor gave me a small dataset with brain Computed Axial Tomography records. I would like to use deep learning to help doctors diagnose a certain disease (obviously, I've also got the data for doing supervised learning).

Since the dataset is small, is radial basis function network a good solution? What do you think?

Btw, if you have any type of tips in using the RBF network for this kind of project I would be really grateful.

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Not familiar with RBF but when you have a small data set image augmentation can help. You can do this easily with the Keras ImageDataGenerator, documentation is here. Alternatively you can create image augmentation yourself using image processing models like PIL or CV2.

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