Considering that traditional edge detection algorithms are difficult to handle blurred medical images, this study proposes an edge detection network ECENet based on deep learning. First, the network is based on the CHRNet model, and its last two layers are pruned to make the model more efficient and lightweight. Secondly, the attention module SKSAM is added to the feature extraction stage of the network to optimize the adaptive extraction of image features and reduce the impact of noise. Finally, context-aware fusion blocks are applied to connect multi-scale network outputs to help the model better understand the structure and semantic information of the image. In addition, considering the pixel-level accuracy and the smoothness of the boundary, the loss function is optimized to provide better gradient signals for model training. Experimental results show that the proposed algorithm increases optimal data set size (ODS) and optimal image ratio (OIS) indicators to 0.816 and 0.823 respectively; the relevant edge indicator parameters were significantly improved, with PSNR increased by 16.8% and SSIM by 37.6%.