Hybrid Nature Inspired SMO-GBM Classifier for Exudate Classification on Fundus Retinal Images - 13/03/19
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Graphical abstract |
Abstract |
Background: The diabetic retinopathy can result in loss of vision if not detected in the earlier stages. Exudates are the lesions which play a crucial role in early diagnosis of diabetic retinopathy. The localization of exudates lesions with high values of performance metrics is complicated due to presence of blood vessels and other noisy artifacts. Method: We present computer aided system for classification of retinal fundus images using a novel nature inspired spider monkey optimization for parameter tuning of gradient boosting machines classifier. The image enhancement has been performed with histogram equalization and contourlet transform. The pixels belonging to optic disc region are detected and eliminated using circular Hough transform and Otsu's segmentation method. We have employed Kirsch's matrices for blood vessel detection. The GLCM based feature vector extraction has been employed for textural features. The classification has been performed with hybrid SMO-GBM classifier. Result: We have utilized the STARE database for validation of proposed technique. The proposed system can effectively classify entire image set from test data. The SMO-GBM classifier can further sub-segregate into sub classes with an average accuracy of 97.5%. Conclusion: The proposed approach provides detection and grading of diabetic retinopathy. The abnormality is further categories as soft, moderate and severe. The hybrid SMO-GBM classifier yields a better statistical metrics than the existing exudates classification approaches.
Le texte complet de cet article est disponible en PDF.Highlights |
• | Hybrid SMO-GBM classifiers based exudate detection approach is proposed. |
• | Parameter tuning of Gradient Boosting Machines yields improved results. |
• | Proposed method outperforms as compared to benchmarked algorithms. |
• | Computer aided system for accurate detection and grading of Diabetic Retinopathy. |
Keywords : Spider monkey optimization, Gradient boosting machines, Fundus retinal images, Hybrid algorithm, Optical disc
Plan
Vol 40 - N° 2
P. 69-77 - mars 2019 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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