Empirical analysis on retinal segmentation using PSO-based thresholding in diabetic retinopathy grading.

Objectives: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapid growth of deep learning eases the clinicians' DR diagnosing procedure. It automatically extracts the features and performs the grading. However, training the image toward the majority of background pixels can impact the accuracy and efficiency of grading tasks. This paper proposes an auto-thresholding algorithm that reduces the negative impact of considering the background pixels for feature extraction which highly affects the grading process.

Methods: The PSO-based thresholding algorithm for retinal segmentation is proposed in this paper, and its efficacy is evaluated against the Otsu, histogram-based sigma, and entropy algorithms. In addition, the importance of retinal segmentation is analyzed using Explainable AI (XAI) to understand how each feature impacts the model's performance. For evaluating the accuracy of the grading, ResNet50 was employed.

Results: The experiments were conducted using the IDRiD fundus dataset. Despite the limited data, the retinal segmentation approach provides significant accuracy than the non-segmented approach, with a substantial accuracy of 83.70 % on unseen data.

Conclusions: The result shows that the proposed PSO-based approach helps automatically determine the threshold value and improves the model's accuracy.