A Novel Iterative Hyperparameter Optimization Method for Enhanced Diabetic Retinopathy Screening

Abstract

A major contributor to blindness in people of working age worldwide is diabetic retinopathy (DR), which is mostly caused by vascular damage brought on by high blood sugar in those who have diabetes mellitus. Since early detection greatly reduces the risk of vision loss, it is essential for the effective management of DR. This work presents a novel method that uses artificial intelligence (AI) to improve fundus image analysis for early diagnosis of diabetes mellitus (DR). Understanding how important high-quality images are to precise diagnosis, our approach starts with a sophisticated preprocessing stage. This stage focuses on improving the pictures by removing erroneous dark patches and other distortions, readying the dataset for additional examination. After preprocessing, we utilize an innovative hybrid hyperparameter optimization model that creatively combines the best features of grid and random search techniques. In order to identify early indicators of DR, such as microaneurysms and vessel changes, this model seeks to optimize the parameters of machine learning algorithms. Through a methodical analysis of the preprocessed fundus images, our model more effectively and accurately detects patterns suggestive of early-stage DR. The results of this study not only show that using AI in conjunction with careful preprocessing and hyperparameter optimization is effective in diagnosing DR, but they also open up new avenues for future investigation into the use of AI in early disease detection. In the end, this study makes a substantial contribution to the field of medical imaging and presents a viable treatment option for preventing vision loss in diabetic patients through early diagnostic intervention.