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Purpose: The aim of this work is to demonstrate that convolutional neuralnetworks (CNN) can be applied to extremely sparse image libraries bysubdivision of the original image datasets. Methods: Image datasets from aconventional digital camera was created and scanning electron microscopy (SEM)measurements were obtained from the literature. The image datasets weresubdivided and CNN models were trained on parts of the subdivided datasets.Results: The CNN models were capable of analyzing extremely sparse imagedatasets by utilizing the proposed method of image subdivision. It wasfurthermore possible to provide a direct assessment of the various regionswhere a given API or appearance was predominant.

Applying convolutional neural networks to extremely sparse image datasets using an image subdivision approach