Improved Deep Neural Network for Robust Diabetic Retinopathy Diagnosis

Diabetic retinopathy diagnosis is often complicated by the varying appearance of lesions over time and the subtle differences between adjacent disease stages. These factors can limit the performance of deep learning models, making it difficult to achieve the desired robustness [5]. To overcome these challenges, this paper introduces an improved deep neural network model for robust diabetic retinopathy diagnosis. The proposed model outperforms state-of-the-art methods by effectively capturing subtle lesion features. The paper is structured as follows: Section 2 reviews existing work on diabetic retinopathy diagnosis. Section 3 details the architecture of our improved deep neural network model. Section 4 presents the experimental results and compares the performance of our model with existing methods. Finally, Section 5 concludes the paper.