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
Segmentation-based Deep Learning Fundus Image Analysis
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. (BigData@BTH)ORCID iD: 0000-0002-4390-411x
2019 (English)In: 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019, IEEE, 2019, article id 8936078Conference paper, Published paper (Refereed)
Abstract [en]

Diabetic retinopathy is the most common cause of new cases of blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus images form an important basis for judging these retinal diseases. To the best of our knowledge, no prior studies have scrutinized the predictive power of the different compositions of retinal images using deep learning. This paper is to investigate whether there exists specific region that could assist in better prediction of the retinopathy disease, meaning to find the best region in fundus images that can boost the prediction power of models for retinopathy classification. To this end, with image segmentation techniques, the fundus image is divided into three different segments, namely, the optic disc, the blood vessels, and the other regions (regions other than blood vessels and optic disk). These regions are then contrasted against the performance of original fundus images. The convolutional neural network as well as transfer deep learning with the state-of-the-art pre-trained models (i.e., AlexNet, GoogleNet, Resnet50, VGG19) are deployed. We report the average of ten runs for each model. Different machine learning evaluation metrics are used. The other regions' segment reveals more predictive power than the original fundus image especially when using AlexNet/Resnet50.

Place, publisher, year, edition, pages
IEEE, 2019. article id 8936078
Keywords [en]
AlexNet, Deep Learning, Fundus Image, Image Segmentation, Retinopathy
National Category
Medical Image Processing Signal Processing Computer Systems
Identifiers
URN: urn:nbn:se:bth-18676DOI: 10.1109/IPTA.2019.8936078ISBN: 9781728139753 (print)OAI: oai:DiVA.org:bth-18676DiVA, id: diva2:1353594
Conference
9th International Conference on Image Processing Theory, Tools and Applications, IPTA; Istanbul; Turkey; 6 November 2019 through 9 November
Funder
Knowledge Foundation, 20140032Available from: 2019-09-23 Created: 2019-09-23 Last updated: 2020-02-06Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full texthttp://www.ipta-conference.com/ipta19/

Authority records BETA

Cheddad, Abbas

Search in DiVA

By author/editor
Qian, WuCheddad, Abbas
By organisation
Department of Computer Science
Medical Image ProcessingSignal ProcessingComputer Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 39 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