###### Abstract  
Eye health has become a significant concern in recent years, given the rising prevalence of visual impairment resulting from various eye disorders and related factors. Global surveys suggest that approximately 2.2 billion individuals are visually impaired, with at least 1 billion affected by treatable diseases or aliments. Early detection, treatment, and screening for fundus diseases are crucial in addressing these challenges. In this study, we propose a novel segmentation model for retinal vascular delineation aimed at diagnosing diabetic retinopathy. The model integrates CBAM (Channel-Attention and Spatial-Attention) for enhanced feature representation, JPU (Joint Pyramid Upsampling) for multi-scale feature fusion, and transformer blocks for contextual understanding. Leveraging deep-learning techniques, our proposed model outperforms existing approaches in retinal vascular segmentation, like achieving a Mean IOU of 0.8047, Recall of 0.7254, Precision of 0.8492, F1 Score of 0.7824, and Specificity of 0.9892 for CHASEDB1 dataset. Extensive evaluations on benchmark datasets demonstrate its efficacy, highlighting its potential for automated diabetic retinopathy screening. Keywords:retinal vessel segmentation; transformer; joint pyramid upsampling; attention mechanisms; multi-scale analysis +
Footnote †: journal: Medical  
+++ ==WARNING: Truncated because of repetitions==
Earlier methods required ophthalmologists to manually segment the data, which was a tedious and time-consuming procedure and involved human errors [16]. Artificial Intelligence (AI) has revolutionized medical image processing by introducing advanced techniques that significantly enhance diagnostic accuracy and efficiency [12; 16; 17]. Because they can automatically learn discriminative features, machine learning (ML) techniques, in particular, Convolutional Neural Networks (CNNs), have become increasingly popular for retinal vascular segmentation [12; 18]. Within the field of DL-based methods, U-Net [19], and its variations have demonstrated effectiveness in medical picture segmentation applications, such as retinal vascular segmentation. Improved segmentation accuracy has been attributed to several models, including the joint loss proposed by [PERSON], [PERSON], and [PERSON] [20] and the vascular segmentation network by [PERSON] et al. [21]. Semantic information capture has been increased by models such as DEU-Net [22] and DeepVessel [23], which use multi-scale convolution blocks. Variants of encoder-decoder networks have been successful, but maintaining feature information between these parts is still a problem [24]. For the purpose of retaining spatial information, feature extraction techniques at the encoder and feature fusion approaches at the decoder have been investigated [12]. Although U-shaped generators and adversarial learning have been studied, more research is needed to integrate these design phases in a methodical way for overall performance enhancement [25]. [PERSON] et al. [26] introduced Attention U-Net, which demonstrated how attention mechanisms can enhance segmentation accuracy by focusing on relevant features. Moreover, [PERSON] et al. [27] utilized a dual-attention mechanism to improve the segmentation of medical images, particularly in challenging regions. Recently, [PERSON] et al. [28] applied Vision Transformers (ViT) to image classification, showing significant improvements in capturing global context compared to CNNs. Furthermore, [PERSON] et al. [29] extended transformers to dense prediction tasks like segmentation, highlighting their ability to model long-range dependencies. In 2017, [PERSON] et al. [30] proposed DeepLab, which incorporates atrous spatial pyramid pooling (ASPP) for capturing multi-scale context. Meanwhile, in 2017, [PERSON] et al. [31] introduced PSPNet, emphasizing pyramid pooling to aggregate global context, improving segmentation performance in varying scales.