Abstract
Background: Cotton-wool spots also referred as soft exudates are the early signs of complications in the eye fundus of the patients suffering from diabetic retinopathy. Early detection of exudates helps in the diagnosis of the disease and provides better medical attention.
Methods: In this paper, an automated system for the detection of soft exudates has been suggested. The system has been developed by the combination of different techniques like Scale Invariant Feature Transform (SIFT), Visual Dictionaries, K-means clustering and Support Vector Machine (SVM).
Results: The performance of the system is evaluated on a publically available dataset and AUC of 94.59% is achieved with the highest accuracy obtained is 94.59%. The experiments are also performed after mixing three datasets and AUC of 92.61% is observed with 91.94% accuracy.
Conclusion: The proposed system is easy to implement and can be used by medical experts both online and offline for referral of Cotton-wool spots in large populations. The system shows promising performance.
Keywords: Cotton-wool spots, diabetic retinopathy, eye fundus, SIFT, SVM, visual dictionary.
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