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Deep Learning-based Lung Triage for Streamlining the Workflow of Radiologists
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The usage of deep learning algorithms such as Convolutional Neural Networks within the field of medical imaging has grown in popularity over the past few years. In particular, these types of algorithms have been used to detect abnormalities in chest x-rays, one of the most commonly performed type of radiographic examination. To try and improve the workflow of radiologists, this thesis investigated the possibility of using convolutional neural networks to create a lung triage to sort a bulk of chest x-ray images based on a degree of disease, where sick lungs should be prioritized before healthy lungs. The results from using a binary relevance approach to train multiple classifiers for different observations commonly found in chest x-rays shows that several models fail to learn how to classify x-ray images, most likely due to insufficient and/or imbalanced data. Using a binary relevance approach to create a triage is feasible but inflexible due to having to handle multiple models simultaneously. In future work it would therefore be interesting to further investigate other approaches, such as a single binary classification model or a multi-label classification model.

Place, publisher, year, edition, pages
2019. , p. 71
Keywords [en]
Deep learning, Convolutional neural networks, Medical Imaging, Neural networks, Artificial intelligence, Image analysis, Radiographic images, Radiology
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-160537ISRN: LIU-ITN-TEK-A-19/043--SEOAI: oai:DiVA.org:liu-160537DiVA, id: diva2:1354635
Subject / course
Media Technology
Uppsok
Technology
Supervisors
Examiners
Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-09-25Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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More languages
Output format
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