The back thermal symmetry identification by pRAM neural networks

A pRAM neural network was implemented as a classifier in order to identify the symmetry of thermal patterns of the back. This classifier has a clinical value in the diagnosis of some diseases. The reinforcement self-organising algorithm was adopted which chooses the codebook vectors of the input classes in {0, 1}ⁿ hyper-space. Five effective features were extracted as condensed representations of the thermal patterns. A three layer 8-input-pRAM neural net classifier was devised and trained with injected gaussian noise. A promising classification result was obtained and compared with that of the linear Fisher algorithm