Ant Lion Optimizer with Deep Transfer Learning Model for Diabetic Retinopathy Grading on Retinal Fundus Images

Diabetic retinopathy (DR) becomes a sight-threatening complication because of diabetes mellitus which affects the retina. Initial identification of DR turns out to be a significant one as it might cause permanent impaired vision in the late stages. The automatic grading of DR seems to have effective benefits in solving such impediments, like rising efficiency, scalability, and coverage of analyzing process, extending applications in developed areas, and enhancing patient prevention by offering premature diagnosis and referral. In recent times, the performances of deep learning (DL) systems in the analysis of DR are close to that of expert-level diagnoses for grading fundus images. This article introduces an Ant Lion Optimizer using ALODTL-DRG technique on retinal fundus images. The presented ALODTL-DRG model performs preprocessing via interpolation image resizing, weighted Gaussian blur, and CLAHE-based contrast enhancement. For feature extraction, Inception with ResNet-v2 model is utilized in this study. At last, the ALO algorithm can be exploited as a hyper parameter tuning strategy to accomplish enhanced DR detection performance. The experimental assessment of the ALODTL-DRG method can be tested by making use of benchmark datasets. A widespread comparison study stated the enhanced performance of the ALODTL-DRG model over recent approaches.