Deep learning based tumor detection and segmentation for automated 3D breast ultrasound imaging
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| Publication date | 2024 |
| Book title | 2024 IEEE South Asian Ultrasonics Symposium conference proceedings (SAUS) |
| Book subtitle | 27-29 March 2024 |
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| ISBN (electronic) |
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| Event | 2024 IEEE South Asian Ultrasonics Symposium, SAUS 2024 |
| Pages (from-to) | 21-24 |
| Number of pages | 4 |
| Publisher | Piscataway, NJ: IEEE |
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| Abstract | Breast cancer is one of the widely diagnosed cancer in the world. However, the detection and segmentation of the tumor is a problem which still needs to be solved. Here we proposed U-Net and YOLO for the segmentation and detection for breast tumor detection in ABUS images. The algorithms were used for 2D images and got a dice score of 0.567 for segmentation and a mAP score of 0.554 for detection of tumor for the split of training data. For validation dataset, the dice score was 0.5388 for segmentation and a detection score of 0.3988 for the detection of tumor in ABUS images. |
| Document type | Conference contribution |
| Language | English |
| Published at | https://doi.org/10.1109/saus61785.2024.10563487 |
| Other links | https://www.proceedings.com/75162.html |
| Downloads |
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