AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses
Diverse breast masses in size, shape and place make accurate image segmentation more challenging in a unified deep-learning network. Therefore, based on the U-net network, an adaptive automatic whole-field segmentation network (AWDS-net) for characterising diverse breast masses is proposed to assist...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2289836 |
| Summary: | Diverse breast masses in size, shape and place make accurate image segmentation more challenging in a unified deep-learning network. Therefore, based on the U-net network, an adaptive automatic whole-field segmentation network (AWDS-net) for characterising diverse breast masses is proposed to assist more accurate and fast medical diagnosis in this paper. In the encoder part of AWDS-net, a small mass extraction mechanism (SMEM) is designed to better retain fine-grained small mass location information, while a spatial pyramid module (SPM) is added to capture multi-scale context and high-resolution image information. In the decoder part, an attention gate (AG) mechanism is inserted to make the model automatically focus on the useful target region information, so that the extracted feature information can be used to build a symmetric encoder-decoder structure for automatic segmentation network of multiple masses in the full field of view. The experimental results on an open-source breast cancer dataset digital database for mammography (DDSM) show that compared with U-net, Attention-Unet, R2U-Net, and SegNet, the proposed AWDS-net achieves, up to higher image segmentation metrics of 3.16% accuracy, 20.59% sensitivity, 5.23% specificity,10.27% precision, 15.08% IoU and 14.21% F1-score with acceptable training time. |
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| ISSN: | 0954-0091 1360-0494 |