Tumor diagnosis using the backpropagation neural network method

Lixing Ma; Sydney Sukuta; Reinhard F. Bruch; Natalia I. Afanasyeva; Carl G. Looney

doi:10.1117/12.306094

24 April 1998 Tumor diagnosis using the backpropagation neural network method

Lixing Ma, Sydney Sukuta, Reinhard F. Bruch, Natalia I. Afanasyeva, Carl G. Looney

Proceedings Volume 3257, Infrared Spectroscopy: New Tool in Medicine; (1998) https://doi.org/10.1117/12.306094
Event: BiOS '98 International Biomedical Optics Symposium, 1998, San Jose, CA, United States

Abstract

For characterization of skin cancer, an artificial neural network method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier transform IR spectrum obtained by a new fiberoptic evanescent wave Fourier transform IR spectroscopy method in the range of 1480 to 1850 cm^-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.

Citation Download Citation

Lixing Ma, Sydney Sukuta, Reinhard F. Bruch, Natalia I. Afanasyeva, and Carl G. Looney "Tumor diagnosis using the backpropagation neural network method", Proc. SPIE 3257, Infrared Spectroscopy: New Tool in Medicine, (24 April 1998); https://doi.org/10.1117/12.306094

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