Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Organ Detection and Localization in Radiological Image Volumes
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Using Convolutional Neural Networks for classification of images and for localization and detection of objects in images is becoming increasingly popular. Within radiology a huge amount of image data is produced and meta data containing information of what the images depict is currently added manually by a radiologist. To aid in streamlining physician’s workflow this study has investigated the possibility to use Convolutional Neural Networks (CNNs) that are pre-trained on natural images to automatically detect the presence and location of multiple organs and body-parts in medical CT images. The results show promise for multiclass classification with an average precision 89.41% and average recall 86.40%. This also confirms that a CNN that is pre-trained on natural images can be succesfully transferred to solve a different task. It was also found that adding additional data to the dataset does not necessarily result in increased precision and recall or decreased error rate. It is rather the type of data and used preprocessing techniques that matter.

Place, publisher, year, edition, pages
2017. , p. 88
Keywords [en]
cnn, deep learning, convolutional neural network, radiology, ct, computed tomography, organ detection, image classification, transfer learning
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-138944ISRN: LIU-IDA/LITH-EX-A--17/024--SEOAI: oai:DiVA.org:liu-138944DiVA, id: diva2:1115425
Subject / course
Information Technology
Presentation
2017-06-09, Alan Turing, Linköping, 10:30 (English)
Supervisors
Examiners
Available from: 2017-07-02 Created: 2017-06-26 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(1772 kB)854 downloads
File information
File name FULLTEXT01.pdfFile size 1772 kBChecksum SHA-512
d69aa7f6d6b5cd4d7d630d119c54a5d74a325252723e530125b69c845bdce50880cffe849867f4838e5282547850728b2bbac0e348869604d6068c2b1ccd0761
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Linder, TovaJigin, Ola
By organisation
Artificial Intelligence and Integrated Computer Systems
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 854 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 3124 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf