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 desig...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 0.8031 and an intersection-over-union(IoU)value of 0.7362.The experimental results show that the proposed enhancement method effectively improves the accuracy and quality of breast cancer lesion region segmentation.展开更多
基金supported by the National Natural Science Foundation of China(No.61961037)the Industrial Support Plan of Education Department of Gansu Province(No.2021CYZC-30)。
文摘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 0.8031 and an intersection-over-union(IoU)value of 0.7362.The experimental results show that the proposed enhancement method effectively improves the accuracy and quality of breast cancer lesion region segmentation.
