—Fundus images play an essential role in ophthalmic diagnostics for the detection of many eye illnesses. The experiment begins with a thorough image preprocessing. technique, which includes clipping the circular borders, scaling the image, enhancing the contrast, removing noise, and augmenting the data. The new combined block applies to extracting distinctive deep feature representations, which help to detect the first shape of the edges of each lesion. It is namely the Attention Block and the Conv-Deconv UNET model. The attention block is subsequently implemented in order to augment the robustness and quality of feature depictions derived from a pair of DR images. The Dual Attention Block for the backbone, which is supplemented with hierarchical Bottleneck attention is what we propose here, referred to as Dual Attention Block UNET (DAB-UNET). Bottleneck Attention Blocks and Dual Attention Blocks greatly improve a model’s ability to concentrate on essential features, boosting its performance in complex tasks such as image segmentation. When these attention mechanisms are built into architectures like DAB-UNET, they make the network faster and more accurate, letting it pick up on small, specific details. This is particularly beneficial in areas like medical imaging, where high precision is essential. In order to emphasize retinal anomalies that are significant for the fovea. macula and Diabetic Retinopathy (DR) semantic segmentation in the deteriorated retina, the network is made up of a unique bottleneck attention block. We trained Mask Region-based Convolutional Neural Network (RCNN) model that comprises of a backbone for eliminating Oculus Dexter (OD) regions. Moreover, the proposed block combines self attention with channel attention in order to highlight these abnormalities. Our results indicate that DAB-UNET is potentially very effective for identifying landmarks even when dealing with different types of retinal degenerative disorders.