Review Article
A Review on Recent Developments for Detection of Diabetic Retinopathy
Table 6
Different methods for detection of microaneurysms.
| Algorithm | Image processing techniques | Database | Color space | Sensitivity | Specificity | Accuracy |
| Sopharak et al. [53] | Median filter, contrast enhancement, Shade Correction, and extended minima transform | Patient data | Green channel | 81.61% | 99.9% | 99.98% |
| Krishna et al. [54] | Ensemble-based microaneurysms detector, Walter Klein, and CLACHE | Messidor | Gray scale | — | — | — |
| Roy et al. [55] | Canny edge detection, morphological reconstruction | DIARETDB1 | Green channel | 89.5% | 82.1% | — |
| Adal et al. [56] | Contrast enhancement technique, Hessian-based candidate selection algorithm, and SVM classifier | ROC | Green channel | — | 44.64% | — |
| Datta et al. [57] | Contrast Limited Adaptive Histogram Equalization, median filter, and Image Catenation | Private data | Green channel | — | 82.64% | 99.98% |
| Giancardo et al. [58] | Radon cliff operator | ROC | Gray scale | 41% | — | — |
| Ding and Ma [59] | Dynamic multiparameter template (DMPT) matching scheme | ROC | | 96% | — | — |
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ROC: retinopathy online challenge.
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