Optical scattering spectra obtained in the clinical trials of breast cancer diagnostic system were analyzed for the purpose
to detect in the dataflow the segments corresponding to malignant tissues. Minimal invasive probe with optical fibers
inside delivers white light from the source and collects the scattering light while being moved through the tissue. The
sampling rate is 100 Hz and each record contains the results of measurements of scattered light intensity at 184 fixed wavelength points. Large amount of information acquired in each procedure, fuzziness in criteria of 'cancer' family membership and data noisiness make neural networks to be an attractive tool for analysis of these data. To define the dividing rule between 'cancer' and 'non-cancer' spectral families a three-layer perceptron was applied. In the process of perceptron learning back propagation method was used to minimize the learning error. Regularization was done using the Bayesian approach. The learning sample was formed by the experts.
End-to-end probability calculation throughout the procedure dataset showed reliable detection of the 'cancer' segments. Much attention was paid on the spectra of the tissues with high blood content. Often the reason is vessel injury caused by the penetrating optical probe. But also it can be a dense vessel net surrounding the malignant tumor. To make the division into 'cancer' and 'non-cancer' families for the tissues with high blood content a special perceptron was learnt exceptionally on such spectra.