Abstract
Breast cancer is the second leading cause of cancer death among women in the United States. If detected and diagnosed in the early stages, it increases survival rates and lowers treatment costs. Emerging deep learning methods have enabled researchers to achieve remarkable results in the classification and segmentation of natural images and computer vision tasks. However, medical images continue to face challenges due to a lack of ground truth for segmentation tasks. As a result, this study proposed and developed automatic methods for segmenting breast lesions from ultrasound images with few label images. The proposed U-NET model employs GELU activation function and has been tested on a total of 620 breast ultrasound images, which includes 310 benign and 310 malignant cases. The quantitative analysis includes accuracy, loss function, dice similarity coefficient, and precision. The qualitative analysis compares original images, masks, image predicted, and the output result, which is mask predicted binary image. The proposed U-NET model outperformed previous methods, achieving a DSC of 99.62%, a loss of 3.01%, an accuracy of 98.15%, and a precision of 91.82%. In addition, we have identified some of the innovations made to the original U-NET model.
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Michael, E., Ma, H., Qi, S. (2022). Breast Tumor Segmentation in Ultrasound Images Based on U-NET Model. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_3
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