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Four Models for Automatic Recognition of Left and Right Eye in Fundus Images
- Source :
- MultiMedia Modeling ISBN: 9783030057091, MMM (1)
- Publication Year :
- 2018
- Publisher :
- Springer International Publishing, 2018.
-
Abstract
- Fundus image analysis is crucial for eye condition screening and diagnosis and consequently personalized health management in a long term. This paper targets at left and right eye recognition, a basic module for fundus image analysis. We study how to automatically assign left-eye/right-eye labels to fundus images of posterior pole. For this under-explored task, four models are developed. Two of them are based on optic disc localization, using extremely simple max intensity and more advanced Faster R-CNN, respectively. The other two models require no localization, but perform holistic image classification using classical Local Binary Patterns (LBP) features and fine-tuned ResNet-18, respectively. The four models are tested on a real-world set of 1,633 fundus images from 834 subjects. Fine-tuned ResNet-18 has the highest accuracy of 0.9847. Interestingly, the LBP based model, with the trick of left-right contrastive classification, performs closely to the deep model, with an accuracy of 0.9718.
- Subjects :
- Left and right
Contextual image classification
Local binary patterns
Computer science
business.industry
Deep learning
Posterior pole
Pattern recognition
02 engineering and technology
Fundus (eye)
03 medical and health sciences
0302 clinical medicine
medicine.anatomical_structure
030221 ophthalmology & optometry
0202 electrical engineering, electronic engineering, information engineering
medicine
020201 artificial intelligence & image processing
Artificial intelligence
Set (psychology)
business
Optic disc
Subjects
Details
- ISBN :
- 978-3-030-05709-1
- ISBNs :
- 9783030057091
- Database :
- OpenAIRE
- Journal :
- MultiMedia Modeling ISBN: 9783030057091, MMM (1)
- Accession number :
- edsair.doi...........c4642b03fd923adcc0b7a38e99838438