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Hierarchically Structured Classification of Carbon Nanostructures from TEM Images by Machine Learning and Computer Vision

Carbon nanotubes (CNT) and carbon nanofibers (CNF) are two widely used carbon nanomaterials for various industrial applications. The airborne particles released from of these materials during the handling and manufacturing of CNT/CNF products in workplaces has potential health impact on humans when exposed through inhalation [1-2]. In order to evaluate the potential exposure hazards of these materials, the airborne nanoparticulate samples were collected and analyzed by transmission electron microscopy (TEM) to determine their types, sizes, and specific morphological properties. After samples were obtained and imaged, individual particles were identified and classified based on aspect ratios and degree of agglomeration, among other descriptors. However, manual identification and classification of nanoscale structures require significant technical expertise and can be highly time-intensive for complex nanostructures [2-3]. Therefore, we introduced transfer learning-based machine learning algorithms and incorporated computer vision approaches to classify a dataset that consisted of 5,323 greyscale TEM images of airborne carbon/non-carbon nanomaterials (see representative images in Fig. 1) to improve classification accuracies and enable automatic processing of nanostructured micrograph data. The primary transfer learning training pipeline [4] for our convolutional neural network (CNN) model was constructed with a vgg16 architecture based on ImageNet pre-trained dataset and followed by using hyper-column representation to collect matrix computation results from five convolutional (Conv) blocks. We constructed a K-means library as a grid and quantified it with vector of locally aggregated descriptors (VLAD) as an encoder, and finally trained with the gradient boosting algorithm for an optimized classifier.