Analysis of the Nosema Cells Identification for Microscopic Images

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dc.contributor.authorDghim, Soumayacs
dc.contributor.authorTravieso-González, Carlos M.cs
dc.contributor.authorBurget, Radimcs
dc.coverage.issue9cs
dc.coverage.volume21cs
dc.date.accessioned2021-11-16T09:52:52Z
dc.date.available2021-11-16T09:52:52Z
dc.date.issued2021-04-28cs
dc.description.abstractThe use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.en
dc.formattextcs
dc.format.extent1-17cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSENSORS. 2021, vol. 21, issue 9, p. 1-17.en
dc.identifier.doi10.3390/s21093068cs
dc.identifier.issn1424-8220cs
dc.identifier.other171368cs
dc.identifier.urihttp://hdl.handle.net/11012/202279
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSENSORScs
dc.relation.urihttps://www.mdpi.com/1424-8220/21/9/3068cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1424-8220/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectimage processingen
dc.subjectNosema diseaseen
dc.subjectmachine learningen
dc.subjectdeep learningen
dc.subjectimageen
dc.subjectdisease detectionen
dc.titleAnalysis of the Nosema Cells Identification for Microscopic Imagesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-171368en
sync.item.dbtypeVAVen
sync.item.insts2021.11.16 10:52:52en
sync.item.modts2021.11.16 10:46:29en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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