Classifying nanostructured and heterogeneous materials from transmission electron microscopy images using convolutional neural networks


Por: Cabrera C., Cervantes D., Muñoz F., Hirata G., Juárez P., Flores D.-L.

Publicada: 1 ene 2022
Resumen:
Artificial intelligence and nanotechnology are two areas of science that have changed the world and made life easier during this last decade. Both fields are undergoing significant knowledge expansion, and both bear the promise of a better future for humankind. This research study used convolutional neural networks to classify images of nanostructured materials of different chemical components, obtained through transmission electron microscopy (TEM). A total of 685 ground truth images from a reduced collection of nanostructured TEM images were analyzed. They were classified into three groups: silicate, silica, and coating, each type belonging to chemical compounds of yttrium silicate, silicon oxide nanoparticles, and silicon oxide nanoparticles as a thin layer (coating), respectively. The classification, location, and segmentation of chemical compounds were conducted using Mask R-CNN (Region-Convolution Neural Network) with ResNet101 as the backbone for convolutional neural networks and trained with the collection of images created. The results showed accuracy scores from 85 to 99% for the three classes. The trained model was also able to classify overlapping and agglomerated clusters of these three compounds. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Filiaciones:
Cabrera C.:
 Universidad Autónoma de Baja California, Carretera Tijuana-Ensenada 3917, Zona Playitas, Baja California, Ensenada, Mexico

 Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Tijuana-Ensenada 3918, Zona Playitas, Baja California, Ensenada, Mexico

Cervantes D.:
 Universidad Autónoma de Baja California, Carretera Tijuana-Ensenada 3917, Zona Playitas, Baja California, Ensenada, Mexico

Muñoz F.:
 Universidad Autónoma de Baja California, Carretera Tijuana-Ensenada 3917, Zona Playitas, Baja California, Ensenada, Mexico

Hirata G.:
 Centro de Nanociencias y Nanotecnología, Carretera Tijuana-Ensenada Km 107, Baja California, Ensenada, Mexico

Juárez P.:
 Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Tijuana-Ensenada 3918, Zona Playitas, Baja California, Ensenada, Mexico

Flores D.-L.:
 Universidad Autónoma de Baja California, Carretera Tijuana-Ensenada 3917, Zona Playitas, Baja California, Ensenada, Mexico
ISSN: 09410643
Editorial
Springer-Verlag London Ltd, 233 SPRING ST, NEW YORK, NY 10013 USA, Estados Unidos America
Tipo de documento: Article
Volumen: 34 Número: 13
Páginas: 11035-11047
WOS Id: 000759356100003