02503nas a2200265   4500000000100000000000100001008004100002260001200043653005800055653004900113653004600162653001400208653001300222653001200235653001700247653000800264100001900272700002100291245012100312856005900433300000900492490001300501520170900514022001402223       9998                            d  c09/202510aAdaptive Scavenger-Based Dingo Optimization Algorithm10aDiabetic Retinopathy Severity Classification10aHigh-Ranking-Based Deep Ensemble Learning10aInception10aResNet5010aResNet910aArchitecture10aVGG1 aA. Jeba Sheela1 aM. Krishnamurthy00aEnsemble Diabetic Retinopathy Severity Classification Framework With Optimized VGG16, Resnet, and Inception Features  u https://www.ijimai.org/journal/bibcite/reference/3597  a1-160 vIn press3 aBackground problem: Diabetic Retinopathy (DR) is characterized by high glucose levels in the blood, which can lead to permanent vision loss and microvascular complications. Various deep learning techniques for DR
analysis tend to be more complex and may experience delays in delivering accurate results, thereby limiting their application in clinical settings. Implementing real-time prediction and severity analysis of DR can address this problem by providing real-time diagnostic insights based on DR severity levels.
Aim: So, this paper is intended to offer a new DR detection and severity classification model with the high-ranking-based ensemble learning approach.
Methodology: The preprocessed and segmented images are utilized in the feature extraction process using ensemble architecture which incorporated VGG16, Resnet, and Inception to get three sets of features. The optimal features are selected using an Adaptive Scavenger-Based Dingo Optimization Algorithm (AS-DOX) to achieve the efficient classification of DR severity. The optimization constraint stake place in the High-
Ranking-Based Deep Ensemble Learning (HR-DEL) model helps to enhance the efficacy of classification for the offered approach. The simulation analysis provides enhanced performance with the accurate classification of the designed DR severity classification approach by comparing it with other baseline methods.
Result: From the result analysis, the offered method achieves 96.6 % accuracy and sensitivity rate. Moreover, it achieves a 90.52% precision rate.
Conclusion: Thus, the designed DR severity classification model attains better performance, and also it is utilized for early detection of DR severity.  a1989-1660