NEURAL-NETWORK CLASSIFICATION OF INFRARED-SPECTRA OF CONTROL AND ALZHEIMERS DISEASED TISSUE

Artificial neural network classification methods were applied to infra red spectra of histopathologically confirmed Alzheimer's diseased and control brain tissue. Principal component analysis was used as a prepr ocessing technique for some of these artificial neural networks while others were trained using the original spectra. The leave-one-out meth od was used for cross-validation and linear discriminant analysis was used as a performance benchmark. In the cases where principal componen ts were used, the artificial neural networks consistently outperformed their linear discriminant counterparts; 100% versus 98% correct class ifications, respectively, for the two class problem, and 90% versus 81 % for a more complex five class problem. Using the original spectra, o nly one of the three selected artificial neural network architectures (a variation of the back-propagation algorithm using fuzzy encoding) p roduced results comparable to the best corresponding principal compone nt cases: 98% and 85% correct classifications for the two and five cla ss problems, respectively.