Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques
Author:
Affiliation:
1. Department of Electrical & Electronics Engineering , PSG College of Technology , Coimbatore , India
2. Department of Electrical & Electronics Engineering , JCT College of Engineering and Technology , Coimbatore , India
Abstract
Publisher
Walter de Gruyter GmbH
Link
https://www.sciendo.com/pdf/10.2478/pjmpe-2019-0018
Reference28 articles.
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2. [2] Sopharak A, Dailey NM, Uyyanonvara B, et al. Machine learning approach to automatic exudate detection in retinal images from diabetic patients. Journal of Modern Optics. 2010;57(2):124-135.
3. [3] Giancardo L, Meriaudeau F, Karnowski TP, et al. Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical Image Analysis. 2012;16(1):216-226.
4. [4] Geetha Ramani R, Balasubramanian L, Jacob SG. Automatic Prediction of Diabetic Retinopathy and Glaucoma through Retinal Image Analysis and Data Mining Techniques. 2012 International Conference on Machine Vision and Image Processing (MVIP), Taipei, 2012, pp. 149-152. IEEE, 201.
5. [5] Wagle S, Mangai JA, Kumar VS. An Improved Medical Image Classification Model using Data Mining Techniques. GCC Conference and exhibition, November 17-20, Doha, Qatar. IEEE, 2013.
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1. Optic Disk Extraction and Hard Exudate Identification in Fundus Images using Computer Vision and Machine Learning;2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC);2021-01-27
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