We report the application of a support vector machine (SVM) for the development of
diagnostic algorithms for optical diagnosis of cancer. Both linear and nonlinear SVMs have …

Background and Objectives A probability‐based, robust diagnostic algorithm is an essential
requirement for successful clinical use of optical spectroscopy for cancer diagnosis. This …

Abstract Background and Objectives Use of laser induced fluorescence (LIF) spectroscopy
for the diagnosis of cancer requires an appropriate diagnostic algorithm for spectral pattern …

An aglorithm based on support vector machines (SVM), the most recent advance in pattern
recognition, is presented for use in classifying light-induced autofluorescence collected from …

We report development of a direct multi-class spectroscopic diagnostic algorithm for
discrimination of high-grade cancerous tissue sites from low-grade as well as precancerous …

In order to establish the benefits of tissue diagnosis based upon optical biopsy, some form of
decision making process is required to determine the pathology. We have compared Neural …

The ability of combining near-infrared (NIR) Raman spectroscopy with support vector
machines (SVM) for improving multi-class classification between different histopathological …

The growing number of applications of Raman spectroscopy in medicine necessitates the
development of robust and accurate processing methods. The two major tasks for which …

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to
investigate the possibility of using IR spectra coupled with multivariate classification …

Significance: The Mueller matrix decomposition method is widely used for the analysis of
biological samples. However, its presumed sequential appearance of the basic optical …