A Cross-Scale Spatial–Channel Attention Inception Network for Efficient Medical Image Segmentation

Abstract

Medical image segmentation plays a crucial role in modern computerized diagnosis, as accurate delineation of anatomical structures directly impacts clinical decision-making and treatment planning. However, segmenting anatomically complex regions at a fine-grained level remains challenging, especially when computational efficiency is a key requirement. To address these challenges, the authors propose a novel, lightweight medical image segmentation framework, CSA-IncepLiteNet, designed to achieve high segmentation accuracy without imposing a significant computational burden. The CSA-IncepLiteNet architecture integrates two key innovations: cross-scale feature extraction and unified spatial channel attention learning. Central to this framework is the newly introduced Cross-Scale InceptionLite module, which efficiently captures multi-scale contextual information. This module is built using depth-wise separable convolutions and point-wise convolutions, enabling effective feature extraction while significantly reducing the number of trainable parameters. By learning features across multiple spatial scales, the network can better represent anatomically complex structures present in medical images. In addition, the authors propose a Cross-Scale Spatial Channel Attention (CSA) module that jointly models spatial saliency and channel-wise interdependencies within a unified attention-learning paradigm. This dual attention mechanism allows the network to focus on the most informative regions and feature channels simultaneously, leading to improved segmentation precision. The performance of CSA-IncepLiteNet was evaluated on the BUSI breast ultrasound dataset and multiple CT image modality-based datasets. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods across all evaluated datasets. Notably, CSA-IncepLiteNet achieves an accuracy of 92.1% and a Dice coefficient of 82.94% on the BUSI dataset, while utilizing over 26 million fewer parameters than a conventional U-Net. These results highlight the model’s effectiveness, robustness, and suitability for resource-constrained medical imaging applications.