Abstract:

To address the challenges of image quality variations and difficulty in lesion area recognition in retinal lesion grading,a novel retinal grading algorithm that integrates PVTv2 and dynamic perception is proposed.Initially,the Eye-Quality dataset undergoes the quality assessment followed by lesion grading.The algorithm first preprocesses the dataset with grayscale conversion and Gaussian filtering to enhance the contrast between lesion regions and background noise.Subsequently,the PVTv2 backbone network performs multi-scale feature extraction on retinal images,achieving the comprehensive capture of multi-scale textural features.Parallel region perception modules and channel reconstruction units are employed to suppress background interference and focus on lesion features,enhancing feature recognition capabilities.A dynamic adaptive feature fusion module establishes connections between global semantic information and edge details.Finally,a hybrid loss function alleviates class imbalance issues,further improving the retinal grading performance.In quality grading experiments on the Eye-Quality dataset,the accuracy and precision are 88.68% and 87.72%,respectively.Then,using accuracy as the evaluation metric for lesion grading based on this foundation,the rates for excellent,usable,and rejectable categories are 80.23%,74.37%,and 73.73%,respectively.These results highlight the impact of image quality differences on lesion grading effectiveness,providing a new avenue for intelligent auxiliary diagnosis in retinal grading.

Key words: lesion grading, quality grading, parallel module, adaptive module