Background: Early diagnosis, monitoring disease progression, and timely treatment of
Diabetic Retinopathy (DR) abnormalities can efficiently prevent visual loss. A prediction system
for the early intervention and prevention of eye diseases is important. The contrast of raw fundus
image is also a hindrance in effective manual lesion detection technique.
Methods: In this research paper, an automated lesion detection diagnostic scheme has been proposed
for early detection of retinal abnormalities of red and yellow pathological lesions. The algorithm
of the proposed Hybrid Lesion Detection (HLD) includes retinal image pre-processing,
blood vessel extraction, optical disc localization and detection stages for detecting the presence of
diabetic retinopathy lesions. Automated diagnostic systems assist the ophthalmologists practice
manual lesion detection techniques which are tedious and time-consuming. Detailed statistical
analysis is performed on the extracted shape, intensity and GLCM features and the optimal features
are selected to classify DR abnormalities. Exhaustive statistical investigation of the proposed
approach using visual and empirical analysis resulted in 31 significant features.
Results: The results show that the HLD approach achieved good classification results in terms of
three statistical indices: accuracy, 98.9%; sensitivity, 97.8%; and specificity, 100% with significantly
Conclusion: The proposed technique with optimal features demonstrates improvement in accuracy
as compared to state of the art techniques using the same database.