## 2 Related Work
Moreover, such approaches typically necessitate extensive medical datasets and substantial computational resources. However, the limited availability of medical data and the constrained computational capabilities of contemporary mobile medical devices collectively hinder the performance of these networks in medical image segmentation tasks. Low-level semantic feature maps play a crucial role not only in capturing detailed spatial information and delineating object boundaries but also in providing essentialpositional information about objects. However, during the downsampling and upsampling processes, this nuanced information may gradually diminish, creating a semantic gap between the encoder and decoder feature maps. To address this issue and enhance the fusion of semantic information, [PERSON] et al. introduced the UNet++ [9] algorithm. This algorithm incorporates a series of nested and densely interconnected networks between the encoder and decoder, significantly enhancing performance in lesion segmentation by merging features at multiple levels. Additionally, [PERSON] et al. developed the UNet3+ [10] algorithm, which captures both fine-grained detail and coarse-grained semantic information across the entire multi-scale spectrum. This is achieved by implementing a full-scale jump connection that directly integrates high-level and low-level semantics from feature maps at various scales. While these methods enhance segmentation capabilities through sophisticated skip connections, they also introduce inefficiencies in segmentation due to their complex network structures. Moreover, the computational demand and parameter count of these algorithms are considerably high. Attention mechanisms can enhance model focus on target regions and minimize the impact of extraneous information, thereby improving segmentation accuracy. The Attention-UNet [11] integrates an attention module within the skip connections of UNet [8], manipulating the significance of features across various spatial locations by generating gating signals that accentuate pertinent features within specific local areas. This integration significantly bolsters the fusion of low-level and high-level semantic features. [PERSON] et al. augmented the efficiency of medical image segmentation by embedding squeeze-and-excitation blocks, ASPP, and attention mechanisms into the Res-Unet++ [25] model. Meanwhile, MCANet [26] tackled the challenge of multi-scale information in conventional ViT-based medical image segmentation by introducing a multi-scale cross-axis attention mechanism, effectively harmonizing information across different scales to enhance segmentation outcomes. The PVT-CASCADE [27] method employs a cascaded attention decoding strategy to elevate segmentation accuracy, which is particularly effective for medical images characterized by complex structures and varying dimensions. This method refines attention allocation, and bolsters feature extraction through a multi-stage process, leading to more precise segmentation outcomes. However, while these networks bolster segmentation precision, their increased complexity necessitates greater computational resources. Such complexity could potentially restrict their applicability in resource-limited settings. Since most of the existing models are usually large in terms of the number of parameters and computation, significant challenges arise when integrating them into resource-constrained mobile medical devices. Consequently, many researchers are dedicated to achieving lightweight models while ensuring performance retention, thereby enhancing the feasibility of practical applications. [PERSON] et al. proposed an efficient convolutional neural network structure optimized for medical image segmentation named ConvUNeXt [15]. This network utilizes enhanced convolutional blocks and finely tuned model depth to improve feature extraction capabilities while reducing computational complexity.