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