AI Article Synopsis
- The combined model shows impressive results with DSC, PPV, and sensitivity values of 0.94, 0.93, and 0.94, indicating strong performance in accurately segmenting breast tumors.
- Our model outperforms other segmentation frameworks, demonstrating its effectiveness in detecting breast tumors.
- The method is adaptable to different types of breast tumors and has the potential for clinical application, aiding in diagnosis and treatment planning for breast cancer patients.
Article Abstract
Results: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors.
Conclusion: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553330 | PMC |
| http://dx.doi.org/10.1155/2022/1770531 | DOI Listing |
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