|Title||Ensembles of radial basis function networks for spectroscopic detection of cervical precancer|
|Publication Type||Peer Reviewed Archived Journal Publications|
|Year of Publication||1998|
|Authors||Tumer, K, Ramanujam, N, Ghosh, J, Richards-Kortum, R|
|Journal||IEEE Trans Biomed Eng|
|ISBN Number||0018-9294 (Print)0018-9294 (Linking)|
|Keywords||*Diagnosis, Computer-Assisted, *Neural Networks (Computer), Algorithms, biopsy, Cervix Uteri/*pathology, Colposcopy, Female, Humans, Mathematical Computing, Multivariate Analysis, Precancerous Conditions/*pathology, Sensitivity and Specificity, Spectrometry, Fluorescence/*methods, Uterine Cervical Neoplasms/*pathology, Vaginal Smears|
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, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous 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 precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.