Quantitative diagnosis of cervical neoplasia using fluorescence lifetime imaging on haematoxylin and eosin stained tissue sections.

The use of conventional fluorescence microscopy for characterizing tissue pathological states is limited by overlapping spectra and the dependence on excitation power and fluorophore concentration. Fluorescence lifetime imaging microscopy (FLIM) can overcome these limitations due to its insensitivity to fluorophore concentration, excitation power and spectral similarity. This study investigates the diagnosis of early cervical cancer using FLIM and a neural network extreme learning machine classifier.