Feed Forward Neural Network for Autofluorescence Imaging Classification

The key elements in cancer diagnostics are the early identification and estimation of the tumor growth anditsspreadinordertodeterminetheareatobeoperatedon. Theaimofourstudywastodevelopnewmethodsof analyzing autofluorescence images which will allow us an objective and accurate assessment of the locationof a tumor and will also be helpful in determining the advancement of the disease. The proposed classificationmethods are based on neural network algorithms. An Olympus company endoscopic system was used for anautofluorescence intestine imaging study. The autofluorescence imaging analysis process can be divided intoseveral main stages. The first step is preparation of a training data set. The second one involves selection offeaturespace, namelytheselectionof thosefeatureswhichenabledistinguishing thepathologicallyalteredareasfromthehealthyones. Finalstagesoftheanalysisincludepathologicallychangedtissueclassificationanddiagnosis.PACSnumbers:87.57.