Detection of cotton wool for diabetic retinopathy analysis using neural network

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dc.contributor.authorBui, Toan
dc.contributor.authorManeerat, Noppadol
dc.contributor.authorWatchareeruetai, Ukrit
dc.date.accessioned2026-08-06T10:18:07Z
dc.date.available2026-08-06T10:18:07Z
dc.date.issued2017-12-13
dc.description.abstractThis paper presents an automatic segmentation method used to detect cotton wool spots in the retinal images for diabetic retinopathy disease. An early detection of cotton wool is important to prevent the dangerous damage which may cause blindness and vision loss. A preprocessing is applied to enhance image quality followed by optic disc removal. A feature extraction method is used to take useful elements from the image for increasing accuracy in classification step. A neural network model is employed for learning task and tested by k-fold cross validation. Our approach is evaluated by ground truth on DIARETDB1 public data. The result shows that cotton wool can be segmented by this method with 85.9% in sensitivity, 84.4% in specificity, and 85.54% in accuracy.
dc.identifier.citation2017 IEEE 10th International Workshop on Computational Intelligence and Applications Iwcia 2017 Proceedings, 2017-December, 203-206, 2017
dc.identifier.doi10.1109/IWCIA.2017.8203585
dc.identifier.other2-s2.0-85047192768
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/8057
dc.source2017 IEEE 10th International Workshop on Computational Intelligence and Applications Iwcia 2017 Proceedings
dc.subjectclassification
dc.subjectdiabetic retinopathy
dc.subjectImage processing
dc.subjectmedical image processing
dc.subjectneural network
dc.titleDetection of cotton wool for diabetic retinopathy analysis using neural network
dc.typeConference Paper

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