A statistical comparison between an unsupervised neural network and a partially connected neural network in the detection of breast cancer

Smaranda Belciug


This paper deals with the comparison of the two neural network methods of learning: supervised (partially connected neural network) and unsupervised (self organizing featuremaps (SOFM), in order to assess their performances on a labeled breast cancer database. A statistical comparison has been made to reveal the diĀ®erences between the two methods regarding diagnosis accuracy and computational time.

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