Retinal OCT Disease Classification
8 papers with code • 2 benchmarks • 2 datasets
Classifying different Retinal degeneration from Optical Coherence Tomography Images (OCT).
Benchmarks
These leaderboards are used to track progress in Retinal OCT Disease Classification
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Libraries
Use these libraries to find Retinal OCT Disease Classification models and implementations
OCTID
RETOUCH
Latest papers with no code
Demystifying Deep Learning Models for Retinal OCT Disease Classification using Explainable AI
In the world of medical diagnostics, the adoption of various deep learning techniques is quite common as well as effective, and its statement is equally true when it comes to implementing it into the retina Optical Coherence Tomography (OCT) sector, but (i)These techniques have the black box characteristics that prevent the medical professionals to completely trust the results generated from them (ii)Lack of precision of these methods restricts their implementation in clinical and complex cases (iii)The existing works and models on the OCT classification are substantially large and complicated and they require a considerable amount of memory and computational power, reducing the quality of classifiers in real-time applications.
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration
Methods: Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR.
