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Convolutional neural networksfor classification of transmissionelectron microscopy imagery
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

One of Vironova's electron microscopy services is to classify liposomes. This includes determining the structure of a liposome and presence of a liposomal encapsulation. A typical service analysis contains a lot of electron microscopy images, so automatic classification is of great interest. The purpose of this project is to evaluate convolutional neural networks for solving lamellarity and encapsulation classification problems. The available data sets are imbalanced so a number of techniques toovercome this problem are studied. The convolutional neural network models have reasonable performance and offer great flexibility, so they can be an alternative to the support vector machines method which is currently used to perform automatic classification tasks. The project also includes the feasibility study of convolutional neural networks from Vironova's perspective.

Place, publisher, year, edition, pages
2017. , p. 55
Series
IT ; 17004
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-322014OAI: oai:DiVA.org:uu-322014DiVA, id: diva2:1095670
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2017-05-15Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
  • en-GB
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More languages
Output format
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