Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks

Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves, blood vessels, retinal health, and the impact of diabetes on the optic nerves. Fundus disorders are a major global health concern, affecting millions of people worldwide due to their widespread occurrence. Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy. As a result, accurate fundus detection is essential for early diagnosis and effective treatment, helping to prevent severe complications and improve patient outcomes. To address this need, this article introduces a Derivative Model for Fundus Detection using Deep Neural Networks (DMFD-DNN) to enhance diagnostic precision. This method selects key features for fundus detection using the least derivative, which identifies features correlating with stored fundus images. Feature filtering relies on the minimum derivative, determined by extracting both similar and varying textures. In this research, the DNN model was integrated with the derivative model. Fundus images were segmented, features were extracted, and the DNN was iteratively trained to identify fundus regions reliably. The goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally, taking into account the least possible derivative across iterations, and using outputs from previous cycles. The hidden layer of the neural network operates on the most significant derivative, which may reduce precision across iterations. These derivatives are treated as inaccurate, and the model is subsequently trained using selective features and their corresponding extractions. The proposed model outperforms previous techniques in detecting fundus regions, achieving 94.98% accuracy and 91.57% sensitivity, with a minimal error rate of 5.43%. It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead, thereby improving operational efficiency and scalability. Ultimately, the proposed model enhances diagnostic precision and reduces errors, leading to more effective fundus dysfunction diagnosis and treatment.