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Classifying DME vs Normal SD-OCT volumes: A review
- Source :
- 23rd International Conference on Pattern Recognition, 23rd International Conference on Pattern Recognition, Dec 2016, Cancun, Mexico, ICPR
- Publication Year :
- 2016
- Publisher :
- HAL CCSD, 2016.
-
Abstract
- International audience; This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison.
- Subjects :
- genetic structures
Computer science
Diabetic macular edema
Early detection
[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing
Machine learning
computer.software_genre
01 natural sciences
010309 optics
03 medical and health sciences
0302 clinical medicine
benchmark
0103 physical sciences
medicine
[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Retina
Blindness
business.industry
Machine Learning (ML)
medicine.disease
eye diseases
Spectral Domain OCT (SD-OCT)
medicine.anatomical_structure
030221 ophthalmology & optometry
Benchmark (computing)
Artificial intelligence
Data mining
sense organs
Diabetic Macular Edema (DME)
business
computer
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- 23rd International Conference on Pattern Recognition, 23rd International Conference on Pattern Recognition, Dec 2016, Cancun, Mexico, ICPR
- Accession number :
- edsair.doi.dedup.....843d8aa48c4fd0605e9b6ed94f3ffd07