  To prepare for identifying the composition of nanowire-fiber mixtures in
Scanning Electron Microscope (SEM) images, we optimize the performance of image
classification between nanowires, fibers and tips due to their geometric
similarities. The SEM images are analyzed by deep learning techniques where the
validation accuracies of 11 convolutional neural network (CNN) models are
compared. By increasing the diversity of data such as reflection, translation
and scale factor approaches, the highest validation accuracy of recognizing
nanowires, fibers and tips is 97.1%. We proceed to classify the level of
porosity in anodized aluminum oxide for the self-assisted nanowire growth where
the validation accuracy is optimized at 93%. Our software allow scientists to
count the percentage of fibers in any nanowire-fiber composite and design the
porous substrate for embedding different sizes of nanowires automatically,
which assists the software development in Nanoscience Foundries & Fine Analysis
(NFFA) Europe Projects.
