HST-S2CBoost: A SELF-SUPERVISED GRADIENT BOOSTED DEEP LEARNING MODEL FOR DIABETIC RETINOPATHY CLASSIFICATION

Diabetes Retinopathy (DR) is a serious complication evolving from long-term diabetes mellitus dysfunction affecting the retinal blood vessels. Early diagnosis and medication are crucial to reduce the severity of DR and avert vision loss. However, the existing DR detection and classification techniques fail to extract the subtle lesion features, resulting in limited accuracy. Consequently, this research proposes the Haliaeetus Search and Tempt optimized Self-supervised DCNN-based extreme gradient Boosting (HST-S2CBoost) framework for improving the DR Classification. Specifically, the proposed research exploits the HST-U2Net-based segmentation for performing the effective segmentation of complex lesions such as microaneurysms and exudates from the retinal blood vessels. Besides, the Haliaeetus Search and Tempt optimization (HSTO) enhances the segmentation and optimally tunes the hyperparameters of the classifier, resulting in accurate DR classification. Further, the Statistical Deep Triangular Heat Pattern (SDTH) is employed in the proposed approach for the extraction of deeper structures, leading to improved DR classification. Moreover, the integration of the advanced strategies in the proposed approach contributes to deep feature extraction, reduces the computation complexity, and provides enhanced DR detection and classification. Experimental results demonstrate that the proposed HST-S2CBoost framework achieves superior performance, obtaining a high accuracy of 97.71%, sensitivity of 95.96%, and specificity of 98.70% with 80% of training utilizing the APTOS dataset. Further, the proposed framework achieves superior results of 96.88%,94.92%, and 97.96% for accuracy, sensitivity, and specificity with 80% of training utilizing the IDRiD dataset.