  Our research focuses on the critical field of early diagnosis of disease by
examining retinal blood vessels in fundus images. While automatic segmentation
of retinal blood vessels holds promise for early detection, accurate analysis
remains challenging due to the limitations of existing methods, which often
lack discrimination power and are susceptible to influences from pathological
regions. Our research in fundus image analysis advances deep learning-based
classification using eight pre-trained CNN models. To enhance interpretability,
we utilize Explainable AI techniques such as Grad-CAM, Grad-CAM++, Score-CAM,
Faster Score-CAM, and Layer CAM. These techniques illuminate the
decision-making processes of the models, fostering transparency and trust in
their predictions. Expanding our exploration, we investigate ten models,
including TransUNet with ResNet backbones, Attention U-Net with DenseNet and
ResNet backbones, and Swin-UNET. Incorporating diverse architectures such as
ResNet50V2, ResNet101V2, ResNet152V2, and DenseNet121 among others, this
comprehensive study deepens our insights into attention mechanisms for enhanced
fundus image analysis. Among the evaluated models for fundus image
classification, ResNet101 emerged with the highest accuracy, achieving an
impressive 94.17%. On the other end of the spectrum, EfficientNetB0 exhibited
the lowest accuracy among the models, achieving a score of 88.33%. Furthermore,
in the domain of fundus image segmentation, Swin-Unet demonstrated a Mean Pixel
Accuracy of 86.19%, showcasing its effectiveness in accurately delineating
regions of interest within fundus images. Conversely, Attention U-Net with
DenseNet201 backbone exhibited the lowest Mean Pixel Accuracy among the
evaluated models, achieving a score of 75.87%.
