A Lightweight Sugar Web Lesion Segmentation Algorithm Based on Asymmetric Skip Connections
Retinopathy(DR)in diabetes is the main cause of blindness.Fundus lesions are a clinical manifestation of DR,therefore,accurate identification is crucial for early screening,grading,and monitoring of the disease.In this article,a lightweight DR lesion segmentation algorithm is proposed for simultaneously segmenting four different DR fundus lesions.In order to fully utilize the multi-scale feature information of the encoder stage,an asymmetric skip connection structure is proposed without significantly increasing network parameters.In order to further refine the features and reduce feature redundancy,attention modules have been added to the above structure.The experimental results on the DDR dataset show that compared to other DR lesion segmentation methods,our algorithm achieves highly competitive segmentation performance while maintaining the minimum number of parameters and the fastest speed.
deep learningdiabetes retinopathyfundus lesion segmentationlightweight convolutional neural network