In this work, we propose to use an artificial neural network to classify limited data
of clinical multispectral and autofluorescence images of skin lesions. Although the
amount of data is limited, the deep convolutional neural network classification of
skin lesions using a multi-modal image set is studied and proposed for the first time.
The unique dataset consists of spectral reflectance images acquired under 526 nm,
663 nm, 964 nm, and autofluorescence images under 405 nm LED excitation. The augmentation
algorithm was applied for multi-modal clinical images of different skin lesion groups
to expand the training datasets. It was concluded from saliency maps that the classification
performed by the convolutional neural network is based on the distribution of the
major skin chromophores and endogenous fluorophores. The resulting classification
confusion matrices, as well as the performance of trained neural networks, have been
investigated and discussed.
