JoWUA

Abstract

Diabetic Retinopathy (DR) is a major cause of vision loss in the world hence the relevance of automated and precise detection systems. This paper introduces a superior method of automated detection of DR on the basis of transfer learning (TL) and ensemble models (ET). The technique uses the APTOS-2019 dataset, which uses five categories of DR severity, namely: No DR, Mild, Moderate, Severe, and Proliferative DR. The research employs two trained deep learning networks, Inception V3 and Xception, that are fine-tuned in order to obtain discriminative features of retinal images. In order to enhance the overall performance of classification, an ensemble approach, with the average and weighted voting is used, to utilize the outputs provided by both models. The performance measures applied to assess the proposed framework include standard performance measures, including accuracy, F1-score, scalability, and patient outcomes. The improvements are significant and the accuracy, F1-score and patient outcomes ratio are 97.94%, 98.41 % and 97.52 % respectively. Also, the scalability ratio is 97.63% and the rate of early diagnosis is 96.84 %, which indicate that the model can be considered robust and efficient and can be used to work with larger data volumes and provide the possibility of timely DR identification. The approach provides a valid, accurate, and generalizable solution to the early diagnosis and treatment planning of DR. High transfer learning methods and ensemble models also guarantee the high level of classification, which is why this approach can be effectively deployed to clinical practice in practice. In addition, the suggested system increases the level of healthcare accessibility as it offers automated solutions to resource-limited settings.