Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Pre-Cancer

TL;DR

This paper explores the use of ensemble radial basis function networks applied to fluorescence spectroscopy data for rapid, non-invasive detection of cervical pre-cancer, showing improved accuracy over traditional methods.

Contribution

It introduces ensemble RBF networks for spectroscopic pre-cancer detection, demonstrating enhanced reliability and potential for real-time clinical application.

Findings

Ensemble RBF networks outperform individual models in accuracy.

Spectroscopy-based method achieves high sensitivity and specificity.

Automated detection is feasible for non-expert use.

Abstract

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.