Deep Learning and Handcrafted Features for Virus Image Classification

In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neu...

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Bibliographic Details
Main Authors:	Loris Nanni, Eugenio De Luca, Marco Ludovico Facin, Gianluca Maguolo
Format:	Article
Language:	English
Published:	MDPI AG 2020-12-01
Series:	Journal of Imaging
Subjects:	virus classification texture descriptors deep learning local binary patterns ensemble of descriptors
Online Access:	https://www.mdpi.com/2313-433X/6/12/143

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record_format	Article
spelling	doaj-09b2771b13534ab5adfa28c8202d406f2020-12-22T00:00:44ZengMDPI AGJournal of Imaging2313-433X2020-12-01614314310.3390/jimaging6120143Deep Learning and Handcrafted Features for Virus Image ClassificationLoris Nanni0Eugenio De Luca1Marco Ludovico Facin2Gianluca Maguolo3Dipartimento di Ingegneria Dell’informazione, University of Padova, via Gradenigo 6, 35131 Padova, ItalyDipartimento di Ingegneria Dell’informazione, University of Padova, via Gradenigo 6, 35131 Padova, ItalyDipartimento di Ingegneria Dell’informazione, University of Padova, via Gradenigo 6, 35131 Padova, ItalyDipartimento di Ingegneria Dell’informazione, University of Padova, via Gradenigo 6, 35131 Padova, ItalyIn this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.https://www.mdpi.com/2313-433X/6/12/143virus classificationtexture descriptorsdeep learninglocal binary patternsensemble of descriptors
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Loris Nanni Eugenio De Luca Marco Ludovico Facin Gianluca Maguolo
spellingShingle	Loris Nanni Eugenio De Luca Marco Ludovico Facin Gianluca Maguolo Deep Learning and Handcrafted Features for Virus Image Classification Journal of Imaging virus classification texture descriptors deep learning local binary patterns ensemble of descriptors
author_facet	Loris Nanni Eugenio De Luca Marco Ludovico Facin Gianluca Maguolo
author_sort	Loris Nanni
title	Deep Learning and Handcrafted Features for Virus Image Classification
title_short	Deep Learning and Handcrafted Features for Virus Image Classification
title_full	Deep Learning and Handcrafted Features for Virus Image Classification
title_fullStr	Deep Learning and Handcrafted Features for Virus Image Classification
title_full_unstemmed	Deep Learning and Handcrafted Features for Virus Image Classification
title_sort	deep learning and handcrafted features for virus image classification
publisher	MDPI AG
series	Journal of Imaging
issn	2313-433X
publishDate	2020-12-01
description	In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.
topic	virus classification texture descriptors deep learning local binary patterns ensemble of descriptors
url	https://www.mdpi.com/2313-433X/6/12/143
work_keys_str_mv	AT lorisnanni deeplearningandhandcraftedfeaturesforvirusimageclassification AT eugeniodeluca deeplearningandhandcraftedfeaturesforvirusimageclassification AT marcoludovicofacin deeplearningandhandcraftedfeaturesforvirusimageclassification AT gianlucamaguolo deeplearningandhandcraftedfeaturesforvirusimageclassification
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Deep Learning and Handcrafted Features for Virus Image Classification

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