Abstract: The present disclosure provides a method, device and equipment for identifying and detecting a macular region in a fundus image. The method includes the following steps: reading a current fundus image to be positioned and detected; detecting a macular region in the fundus image using a target detection model; when the macular region in the fundus image is not detected, detecting an optic disk region in the fundus image, and identifying and positioning the macular region based on the detected optic disk region; based on a positioning result of the macular region, extracting a macular image corresponding to the macular region from the fundus image; and performing multi-modal processing on the macular image, fusing images obtained by the multi-modal processing to obtain a fused image, and detecting whether the macular region is qualified or not according to the fused image.

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.

Abstract: The present disclosure relates to a method and device for recognizing a fundus image, and equipment. The method includes: obtaining an acquired fundus image, and pre-processing the acquired fundus image; inputting the pre-processed fundus image to a trained optic disc (OD) prediction model, and performing OD prediction on the fundus image by the OD prediction model to obtain a corresponding OD prediction map, where the OD prediction map is marked with one or more located candidate areas of the OD; and obtaining the OD prediction map and performing ellipse fitting to obtain ellipse fitting parameters of the candidate areas of the OD, and determining an OD area in the fundus image based on the number of the candidate areas of the OD and the ellipse fitting parameters.