  The mortality related to cervical cancer can be substantially reduced through
early detection and treatment. However, current detection techniques, such as
Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and
specificity.
  In vivo fluorescence spectroscopy is a technique which quickly,
non-invasively and quantitatively probes the biochemical and morphological
changes that occur in pre-cancerous tissue.
  A multivariate statistical algorithm was used to extract clinically useful
information from tissue spectra acquired from 361 cervical sites from 95
patients at 337, 380 and 460 nm excitation wavelengths. The multivariate
statistical analysis was also employed to reduce the number of fluorescence
excitation-emission wavelength pairs required to discriminate healthy tissue
samples from pre-cancerous tissue samples. The use of connectionist methods
such as multi layered perceptrons, radial basis function networks, and
ensembles of such networks was investigated. RBF ensemble algorithms based on
fluorescence spectra potentially provide automated, and near real-time
implementation of pre-cancer detection in the hands of non-experts. The results
are more reliable, direct and accurate than those achieved by either human
experts or multivariate statistical algorithms.
