Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks
Autor: | Shrouq Aleithan, Doaa Mahmoud-Ghoneim |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Mathematics and computing
Computer science lcsh:Medicine 02 engineering and technology 010402 general chemistry 01 natural sciences Article Nanomaterials Nanoscience and technology Monolayer lcsh:Science Multidisciplinary Artificial neural network business.industry Physics lcsh:R Pattern recognition 021001 nanoscience & nanotechnology Linear discriminant analysis Materials science 0104 chemical sciences lcsh:Q Artificial intelligence 0210 nano-technology business Classifier (UML) |
Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-8 (2020) Scientific Reports |
ISSN: | 2045-2322 |
Popis: | The need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class. |
Databáze: | OpenAIRE |
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