A lightweight image segmentation network leveraging inception and squeeze-excitation modules for efficient skin lesion analysis

The U-shaped network (U-Net) and its derivatives are widely regarded as the cornerstone of medical image segmentation, with performance often improved by increasing model depth and complexity. However, this results in a greater computational burden and slower inference, limiting practical deployment. To address these issues, we propose a lightweight image segmentation based on the convolutional multilayer perceptron (MLP)-based network with U-Net (IS-UNeXt) model, a lightweight segmentation model based on an MLP framework that incorporates Inception-inspired multi-scale fusion blocks and squeeze-and-excitation (SE) modules to mitigate key limitations of existing models, such as high computational complexity, excessive parameter size, and high inference time. Evaluated on the international skin imaging collaboration 2018 (ISIC2018) and the dermoscopic image database acquired at the dermatology service of Hospital Pedro Hispano, Portugal (PH2) datasets, IS-UNeXt reduces inference time by 58.7%, parameters by 37.7%, and computational complexity by 48.4% compared to the convolutional MLP-based network with U-Net (UNeXt), while reaching an intersection over union (IoU) of 81.1% and a dice coefficient (DC) of 88.9% on ISIC2018 and IoU of 90.34% and DC of 94.42% on PH2. These results demonstrate IS-UNeXt's effectiveness and efficiency in skin lesion segmentation, rendering it highly suitable for real-time medical applications on resource-constrained devices.