Spectroscopic detection of cervical pre-cancer using radial basis

Abstract

An apparatus and methods for spectroscopic detection of tissue abnormality, particularly precancerous cervical tissue, using neural networks to analyze in vivo measurements of fluorescence spectra. The invention excites fluorescence intensity spectra in both normal and abnormal tissue. This fluorescence spectroscopy data is used to train a group (ensemble) of neural networks, preferably radial basis function (RBF) neural networks. Once trained, fluorescence spectroscopy data from unknown tissue samples is classified by the trained neural networks. This process is used to differentiate pre-cancers from normal tissues, and can also be used to differentiate high grade pre-cancers from low grade pre-cancers. One embodiment of the invention is able to distinguish pre-cancerous tissue from both normal squamous tissue (NS) and normal columnar (NC) tissue in a single-stage of analysis. The invention demonstrates significantly smaller variability in classification accuracy, resulting in more reliable classification, with superior sensitivity. Moreover, the single-stage embodiment of the invention simplifies the decision-making process as compared to a two-stage embodiment.