Evaluation of healthy and infected cervical tissue using a LIFS system and a back-propagation neural network
Abstract
Laser-Induced Fluorescence Spectroscopy (LIFS) is a technique that has been recently used to detect in vivo or in vitro cancer. The LIFS system has been used to analyze cervical tissue samples for histological evaluation. Because the fluorescence spectra are position and inter-probe dependent (albeit they were equally classified by histological evaluation) the relationship that exists in the data is complex. So, a back-propagation Artificial Neural Network is used to detect the relationships contained within them. The validation of the system was done with 5 different proves and a 100% classification coincidence between the neural network classification and a normal histological one was obtained. The LIFS-System works with a N2 laser of 5 µJ pulse energy (tFWHM = 3.8 ns at 337.1 nm wavelength) and spectra from 350 to 650 nm were processed and evaluated in a PC.
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