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11 July 2019 Towards to deep neural network application with limited training data: synthesis of melanoma's diffuse reflectance spectral images
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Abstract
The goal of our study is to train artificial neural networks (ANN) using multispectral images of melanoma. Since the number of multispectral images of melanomas is limited, we offer to synthesize them from multispectral images of benign skin lesions. We used the previously created melanoma diagnostic criterion p'. This criterion is calculated from multispectral images of skin lesions captured under 526nm, 663nm, and 964nm LED illumination. We synthesize these three images from multispectral images of nevus so that the p' map matches the melanoma criteria (the values in the lesion area is >1, respectively). Demonstrated results show that by transforming multispectral images of benign nevus is possible to get a reliable multispectral images of melanoma usable for ANN training.
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Katrina Bolochko, Dmitrijs Bliznuks, Dilshat Uteshev, Ilze Lihacova, Alexey Lihachev, Yuriy Chizhov, and Andrey Bondarenko "Towards to deep neural network application with limited training data: synthesis of melanoma's diffuse reflectance spectral images", Proc. SPIE 11074, Diffuse Optical Spectroscopy and Imaging VII, 110741O (11 July 2019); https://doi.org/10.1117/12.2527173
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