MSGU-Net: a lightweight multi-scale ghost U-Net for image segmentation

• Published in: Frontiers in neurorobotics 2025-01, Vol.18
• Main Authors: Cheng, Hua, Zhang, Yang, Xu, Huangxin, Li, Dingliang, Zhong, Zejian, Zhao, Yinchuan, Yan, Zhuo
• Format: Article
• Language: English
• Subjects: Accuracy; Computer vision; Datasets; Deep learning; Design; Efficiency; Image processing; image segmentation; lightweight neural network; Medical imaging; multi-scale; Natural language processing; Neural networks; SPP-Inception; U-Net
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2024.1480055

U-Net and its variants have been widely used in the field of image segmentation. In this paper, a lightweight multi-scale Ghost U-Net (MSGU-Net) network architecture is proposed. This can efficiently and quickly process image segmentation tasks while generating high-quality object masks for each object. The pyramid structure (SPP-Inception) module and ghost module are seamlessly integrated in a lightweight manner. Equipped with an efficient local attention (ELA) mechanism and an attention gate mechanism, they are designed to accurately identify the region of interest (ROI). The SPP-Inception module and ghost module work in tandem to effectively merge multi-scale information derived from low-level features, high-level features, and decoder masks at each stage. Comparative experiments were conducted between the proposed MSGU-Net and state-of-the-art networks on the ISIC2017 and ISIC2018 datasets. In short, compared to the baseline U-Net, our model achieves superior segmentation performance while reducing parameter and computation costs by 96.08 and 92.59%, respectively. Moreover, MSGU-Net can serve as a lightweight deep neural network suitable for deployment across a range of intelligent devices and mobile platforms, offering considerable potential for widespread adoption.