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<p>In order to solve the challenge of breast cancer region segmentation, we improved the U-Net. The convolutional block attention module with prioritized attention (CBAM-PA) and dilated transformer (Dformer) modules were designed to replace the convolutional layers at the encoding side in the base U-Net, the input logic of the U-Net was improved by dynamically adjusting the input size of each layer, and the short connections in the U-Net were replaced with crosslayer connections to enhance the image restoration capability at the decoding side. On the breast ultrasound images (BUSI) dataset, we obtain a <i>Dice</i> coefficient of 0.803 1 and an intersection-over-union (<i>IoU</i>) value of 0.736 2. The experimental results show that the proposed enhancement method effectively improves the accuracy and quality of breast cancer lesion region segmentation.</p>

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F-Net: breast cancerous lesion region segmentation based on improved U-Net

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Xiangyu Deng,
  • Lihao Pan,
  • Zhiyan Dang

摘要

In order to solve the challenge of breast cancer region segmentation, we improved the U-Net. The convolutional block attention module with prioritized attention (CBAM-PA) and dilated transformer (Dformer) modules were designed to replace the convolutional layers at the encoding side in the base U-Net, the input logic of the U-Net was improved by dynamically adjusting the input size of each layer, and the short connections in the U-Net were replaced with crosslayer connections to enhance the image restoration capability at the decoding side. On the breast ultrasound images (BUSI) dataset, we obtain a Dice coefficient of 0.803 1 and an intersection-over-union (IoU) value of 0.736 2. The experimental results show that the proposed enhancement method effectively improves the accuracy and quality of breast cancer lesion region segmentation.