Deep learning to classify nanostructured materials with heterogeneous composition from transmission electron microscopy images

Carlos Cabrera Sr.; Patricia Juárez; David Cervantes; Franklin Muñoz; Gustavo Hirata; Dora Luz Flores

doi:10.1117/12.2568626

20 August 2020 Deep learning to classify nanostructured materials with heterogeneous composition from transmission electron microscopy images

Carlos Cabrera Sr., Patricia Juárez, David Cervantes, Franklin Muñoz, Gustavo Hirata, Dora Luz Flores

Author Affiliations +

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691P (2020) https://doi.org/10.1117/12.2568626
Event: SPIE Nanoscience + Engineering, 2020, Online Only

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Abstract

This work uses a deep learning approach using convolutional neural networks to locate and classify nanostructures in a heterogenous composition material from TEM imaging. We developed a methodology that allowed us to create 533 ground truth of TEM images with three different classes: 1) silicon oxide nanoparticles, 2) yttrium silicate particles and 3) silicon oxide coating. We performed the classification, location, and segmentation of chemical compounds reaching scores above 80% of accuracy using Mask R-CNN architecture with Anaconda Python 3.7 and the Tensorflow framework under Windows 10.

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Carlos Cabrera Sr., Patricia Juárez, David Cervantes, Franklin Muñoz, Gustavo Hirata, and Dora Luz Flores "Deep learning to classify nanostructured materials with heterogeneous composition from transmission electron microscopy images", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691P (20 August 2020); https://doi.org/10.1117/12.2568626

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