U2-MNet: An Improved Neural Network for Breast Tumor Segmentation

Abstract

In recent years, UNet and its variants have achieved excellent performance. However, since the convolution kernel in UNet focuses solely on local pixels, these models struggle to model long-range dependencies. This issue has been addressed by recently proposed segmentation models based on transformer architecture. The internal self-attention mechanisms included in these systems capture global contextual information to improve segmentation effects. However, the results are often not ideal without pre-training on a large-scale dataset. Therefore, we designed a model (U2-MNet) to overcome the limitations of convolutional kernels, enabling the model to achieve high-accuracy segmentation without pre-training. This approach adopts a multi-layer framework, which can effectively describe multi-level channel information, employing a window-based channel MLP (WCM) block. It utilizes a sliding window and an MLP to capture information about details in local features. In addition, multi-level channel cross mixing (MCCM) block is included in each skip connection to reduce noise after aggregation of low-level and high-level features. The proposed model was trained from scratch and tested on Breast UltraSound Images (BUSI) dataset. Our model achieved 82.76%, 73.17%, and 86.24% on the Dice, IOU, and Precision indicators.