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Moumita Paul
Monisha Chakraborty


Artificial neural networks may be deployed to diagnose illnesses including mental illness. One such mental illness is schizophrenia which is characterized by persistent delusions, hallucinations, disorganized speech, highly disorganized or catatonic behaviour and negative symptoms. It is commonly believed that one or two hidden layers in a neural network are sufficient to classify data, and that more hidden layers may be avoided because of longer times taken for the network to converge. However, we demonstrate that beyond a certain size of the hidden layer(s), it is harmful to deploy more than one layer because not only will it take longer for the network parameters to converge, the classification performance deteriorates sharply with more than one hidden layers.


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