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A Deep Learning Approach for Molecular Classification Based on AFM Images
In spite of the unprecedented resolution provided by non-contact atomic force microscopy (AFM) with CO-functionalized and advances in the interpretation of the observed contrast, the unambiguous identification of molecular systems solely based on AFM images, without any prior information, remains an...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306777/ https://www.ncbi.nlm.nih.gov/pubmed/34202532 http://dx.doi.org/10.3390/nano11071658 |
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author | Carracedo-Cosme, Jaime Romero-Muñiz, Carlos Pérez, Rubén |
author_facet | Carracedo-Cosme, Jaime Romero-Muñiz, Carlos Pérez, Rubén |
author_sort | Carracedo-Cosme, Jaime |
collection | PubMed |
description | In spite of the unprecedented resolution provided by non-contact atomic force microscopy (AFM) with CO-functionalized and advances in the interpretation of the observed contrast, the unambiguous identification of molecular systems solely based on AFM images, without any prior information, remains an open problem. This work presents a first step towards the automatic classification of AFM experimental images by a deep learning model trained essentially with a theoretically generated dataset. We analyze the limitations of two standard models for pattern recognition when applied to AFM image classification and develop a model with the optimal depth to provide accurate results and to retain the ability to generalize. We show that a variational autoencoder (VAE) provides a very efficient way to incorporate, from very few experimental images, characteristic features into the training set that assure a high accuracy in the classification of both theoretical and experimental images. |
format | Online Article Text |
id | pubmed-8306777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83067772021-07-25 A Deep Learning Approach for Molecular Classification Based on AFM Images Carracedo-Cosme, Jaime Romero-Muñiz, Carlos Pérez, Rubén Nanomaterials (Basel) Article In spite of the unprecedented resolution provided by non-contact atomic force microscopy (AFM) with CO-functionalized and advances in the interpretation of the observed contrast, the unambiguous identification of molecular systems solely based on AFM images, without any prior information, remains an open problem. This work presents a first step towards the automatic classification of AFM experimental images by a deep learning model trained essentially with a theoretically generated dataset. We analyze the limitations of two standard models for pattern recognition when applied to AFM image classification and develop a model with the optimal depth to provide accurate results and to retain the ability to generalize. We show that a variational autoencoder (VAE) provides a very efficient way to incorporate, from very few experimental images, characteristic features into the training set that assure a high accuracy in the classification of both theoretical and experimental images. MDPI 2021-06-24 /pmc/articles/PMC8306777/ /pubmed/34202532 http://dx.doi.org/10.3390/nano11071658 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Carracedo-Cosme, Jaime Romero-Muñiz, Carlos Pérez, Rubén A Deep Learning Approach for Molecular Classification Based on AFM Images |
title | A Deep Learning Approach for Molecular Classification Based on AFM Images |
title_full | A Deep Learning Approach for Molecular Classification Based on AFM Images |
title_fullStr | A Deep Learning Approach for Molecular Classification Based on AFM Images |
title_full_unstemmed | A Deep Learning Approach for Molecular Classification Based on AFM Images |
title_short | A Deep Learning Approach for Molecular Classification Based on AFM Images |
title_sort | deep learning approach for molecular classification based on afm images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306777/ https://www.ncbi.nlm.nih.gov/pubmed/34202532 http://dx.doi.org/10.3390/nano11071658 |
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