Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloNeural Network for Nanoscience Scanning Electron Microscope Image Recognition
Anno di pubblicazione2017
Formato-
Autore/iModarres, Mohammad Hadi; Aversa, Rossella; Cozzini, Stefano; Cozzini, Stefano; Ciancio, Regina; Leto, Angelo; Brandino, Giuseppe Piero
Affiliazioni autoriUniversity of Cambridge; Scuola Internazionale Superiore di Studi Avanzati; EXact-Lab Srl; Laboratorio Nazionale TASC; Elegans.io Ltd
Autori CNR e affiliazioni
  • ROSSELLA AVERSA
  • REGINA CIANCIO
  • STEFANO COZZINI
Lingua/e
  • inglese
AbstractIn this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-Automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.
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RivistaScientific reports (Nature Publishing Group)
Attiva dal 2011
Editore: Nature Publishing Group - London
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 2045-2322
Titolo chiave: Scientific reports (Nature Publishing Group)
Titolo proprio: Scientific reports (Nature Publishing Group)
Numero volume della rivista7
Fascicolo della rivista1
DOI10.1038/s41598-017-13565-z
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85031771830)
Parole chiavenanoscience SEM nanoimages deep learning
Link (URL, URI)http://www.scopus.com/record/display.url?eid=2-s2.0-85031771830&origin=inward
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  • IOM — Istituto officina dei materiali
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Neural Network for Nanoscience Scanning Electron Microscope Image Recognition (documento privato )
Tipo documento: application/pdf