###### Abstract  
In recent years, various deep-learning methodologies have been developed for processing medical images, with Unet and its derivatives proving particularly effective in medical image segmentation. Our primary objective is to enhance the accuracy of these networks while also reducing the number of parameters and computational demands to facilitate deployment on mobile medical devices. To this end, we introduce a novel medical image segmentation network, MSLUnet, which aims to minimize parameter count and computational load without compromising segmentation effectiveness. The network features a U-shaped architecture. In the encoder module, we utilize multiple small convolutional kernels for successive convolutions rather than large ones, allowing for capturing multi-scale feature information at granular levels through varied receptive field scales. In the decoder module, an inverse bottleneck structure with depth-separable convolution employing large kernels is incorporated. This design effectively extracts spatial dimensional information and ensures a comprehensive integration of both shallow and deep features. Additionally, a lightweight three-branch attention mechanism within the skip connections enhances information transfer by capturing global contextual data across spatial and channel dimensions. Experimental evaluations conducted on several publicly available medical image datasets indicate that MSLUnet is more competitive than existing models in terms of efficiency and effectiveness. medical image segmentation; multi-scale feature extraction; deep learning; convolutional neural networks (CNNs) +
Footnote †: journal: Medical Image Segmentation  
(FCN) [7], [PERSON] et al. developed UNet [8], a fully convolutional neural network tailored for automated medical image segmentation. UNet [8] is renowned for its unique encoder-decoder structure and skip connections and has shown promising results in this field. However, its convolutional encoding block is relatively simplistic and lacks robust feature extraction capabilities. Furthermore, the basic connection mechanism fails to effectively bridge the semantic gap between the encoder and decoder. To address these limitations, enhancements and variations such as Unet++ [9], Unet3+ [10], Attention UNet [11], ResNet [12], DoubleU-Net [13], UNeXt [14], and ConvUNeXt [15] have been developed, each contributing novel attributes to the domain of medical image segmentation. Automatic semantic segmentation of medical images presents challenges due to complex characteristics such as low contrast, fuzzy boundaries, class imbalance, and similar intensities among organs, tissues, and lesions, along with variations in location, shape, and size. Most existing segmentation methods focus on enhancing performance; however, this often results in models that are either too complex or have an excessive number of parameters, making them unsuitable for deployment on most mobile medical devices. In this study, the MSLUnet model introduces innovative multi-scale feature extraction strategies and attention mechanisms for medical image segmentation. This approach optimizes the processing of image details and reduces the number of parameters, thereby providing an efficient and robust solution. The encoder and decoder modules of MSLUnet have been redesigned to enhance feature extraction efficiency. The encoder module incorporates a symmetric multi-scale feature extraction mechanism, primarily utilizing a single \(3\times 3\) basic convolutional block. This configuration allows for the acquisition of both larger-scale coarse-grained and smaller-scale fine-grained features while minimizing the number of parameters. In the decoder module, deep convolution with large kernels and point-by-point convolution using an inverse bottleneck design replace traditional methods, enhancing the comprehensive extraction of image features. Additionally, a lightweight three-branch attention mechanism introduced in the skip connections aims to more effectively integrate spatial and channel features.