Segmentation of diabetic retinopathy images using deep feature fused residual with U-Net

Abstract

Diabetic Retinopathy (DR) is a highly consequential condition that significantly impacts individuals with diabetes, often resulting in permanent vision loss if left undetected. The accurate assessment of DR severity heavily relies on the precise segmentation of lesions from fundus images. However, manual segmentation processes are complex and time-consuming. In order to address these limitations, this research introduces a novel approach namely Deep Feature Fused Residual with U-Net (DFFR-U-NET) for the segmentation of DR lesions, specifically Haemorrhage (HM), Hard Exudates (HE), and Optic Disc (OD). The proposed method employs a Convolutional Neural Network (CNN) architecture, which incorporates the U-Net model enhanced with a modified bottleneck using residual blocks. This modified U-Net model, composed of robust convolutional blocks and a modified bottleneck, yields optimal results in the segmentation of DR lesions. The training and validation of the proposed method are conducted using the IDRiD dataset, focusing on HM, HE, and OD segmentation. The experimental results demonstrate that the proposed method achieves a high level of accuracy in the segmentation of HM, HE, and OD lesions. Model performance is assessed using key metrics such as accuracy, precision, Mean Intersection Over Union (IOU), Mean Dice Coefficient, and Average Hausdorff distance. The analysis reveals that the proposed model attains an accuracy of 98% for HM, 99% for HE, and 99% for OD. Precision scores are 98% for HM, 99% for HE, and 99% for OD. Moreover, the Mean IOU values are 0.91 for HM, 0.95 for HE, and 0.99 for OD. The Mean Dice Coefficient for HM is 0.91, for HE is 0.95, and for OD is 0.99. Additionally, the Average Hausdorff distance for the background is 2.84 for HM, 1.24 for HE, and 0.38 for OD, while for lesions, it is 2.84 for HM, 1.24 for HE, and 0.38 for OD. When compared to existing studies, the proposed approach establishes a state-of-the-art performance in retinal lesion segmentation, highlighting its superiority in this domain.