Unet retinal vessel image segmentation algorithm based on coordinate attention mechanism and Min-ASPP module optimisation

Abstract

In this paper, the Unet image segmentation algorithm is optimised and improved by introducing the coordinate attention mechanism and integrating the Min-ASPP module, and compared with the traditional Unet model, targeting the segmentation of retinal blood vessel images, which is a key problem in the field of medical image processing. The experimental results show that the optimised Unet algorithm combining the coordinate attention mechanism and Min-ASPP module has a smoother performance in the loss curve, faster convergence speed, and lower final converged loss value, which has an obvious advantage compared with the traditional Unet algorithm. Meanwhile, the optimised Unet algorithm shows higher accuracy and stability in the prediction results, providing ophthalmologists with more reliable information about the retinal vascular structure. Therefore, the research results of this paper show that the Unet image segmentation algorithm optimised by combining the coordinate attention mechanism and Min-ASPP module has better application prospects and effects in the retinal blood vessel image segmentation task, which provides a useful exploration and practice for improving the level of medical image processing technology.