Method and system for detecting fundus image based on dynamic weighted attention mechanism

Abstract

The present disclosure provides a method and system for detecting a fundus image based on a dynamic weighted attention mechanism. Lesion information in a fundus image of a premature infant is detected using a fundus image segmentation model. First, the fundus image is consecutively downsampled. Dynamical weighted attention fusion is performed on an obtained downsampling feature and an obtained downsampling feature of an adjacent layer. The weighted and fused features are fused with an output feature of a corresponding upsampling layer. Finally, a classification convolution operation is performed on an output of an n-th upsampling layer to obtain a lesion probability for each pixel. The present disclosure performs hierarchical feature fusion on a shallow network model using the dynamic weighted attention mechanism, which can reduce complexity of algorithm design, shorten a running time of an algorithm, and reduce excessive occupation of graphics processing unit (GPU) resources while ensuring recognition accuracy.