  Mycelium is a promising biomaterial based on fungal mycelium, a highly
porous, nanofibrous structure. Scanning electron micrographs are used to
characterize its network, but the currently available tools for nanofibrous
microstructures do not contemplate the particularities of biomaterials. The
adoption of a software for artificial nanofibrous in mycelium characterization
adds the uncertainty of imaging artifact formation to the analysis. The
reported work combines supervised and unsupervised machine learning methods to
automate the identification of artifacts in the mapped pores of mycelium
microstructure.
  Keywords: Machine learning; unsupervised learning; image processing;
mycelium; microstructure informatics
