Lightweight LDA-UNet for accurate and interpretable visualization of retinopathy of prematurity

Retinopathy of prematurity (ROP) is one of the main causes of blindness in children. Timely prevention and treatment can effectively control its progression. Although convolutional neural networks (CNNs) have been widely studied in the field of ROP detection, limitations such as parameter redundancy, spatial information loss, and limited interpretability persist. To address these issues, we proposed LDA-UNet, a lightweight CNN model that integrates depth separable convolution, multi-scale dilated convolution, and residual channel-spatial attention mechanisms. Our model significantly reduces parameters (by 85.4% compared to standard UNet) while alleviating the lack of spatial information loss during the encoding stage. The Dice coefficient is 0.927, which exceeds both the current typical lightweight UNet models and the Transformer-based hybrid structure TransUNet. By combining LDA-UNet with a contour detection algorithm, we introduce an object visualization model that precisely locates lesion areas and delineates contours, enhancing interpretability. Our approach outperforms YOLOv5s in mean Intersection over Union (mIoU) by 17.5% and object detection accuracy by 12.5%, underscoring its efficacy in ROP visualization. This work presents a robust, lightweight solution for ROP diagnosis, facilitating clinical decision-making and improving patient outcomes. Codes are available at https://github.com/honghaolu/ROP_Visualizaiton or https://doi.org/10.5281/zenodo.13922434 .