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Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification

Aversa, Rossella ORCID iD icon; Coronica, Piero; De Nobili, Cristiano; Cozzini, Stefano


In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from 1 $\mu$m to 2 $\mu$m). Finally, we compare different clustering methods to uncover intrinsic structures in the images.

Verlagsausgabe §
DOI: 10.5445/IR/1000137122
Veröffentlicht am 02.09.2021
DOI: 10.1162/dint_a_00062
Zitationen: 8
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Steinbuch Centre for Computing (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 10.2020
Sprache Englisch
Identifikator ISSN: 2641-435X
KITopen-ID: 1000137122
Erschienen in Data Intelligence
Verlag MIT Press
Band 2
Heft 4
Seiten 513–528
Bemerkung zur Veröffentlichung This work has been done within the NFFA-EUROPE project and has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 654360 NFFA-EUROPE.
Schlagwörter Neural networks, Feature learning, Clustering analysis, Scanning Electron Microscope (SEM), Image classification
Nachgewiesen in Dimensions
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