A Novel Deep Learning Approach: Breast Cancer Segmentation Using HCMA-U Net
Keywords:
Breast cancer segmentation, BUSI, Deep learning, HCMA-U net, MISM, Tumor, U-netAbstract
Breast cancer continue to exist as a major widespread and mortal diseases globally that have impact on women. Early detection is vital to enhance the likelihood of survival. The proposed
Hybrid Convolutional multi-Attention U-Net (HCMA_UNet) is a novel model used for segmentation of breast cancer ultrasound images. The model incorporates Residual Blocks, Multi-Instance Self-Modification (MISM), and Attention Mechanisms to improve accuracy of feature extraction and segmentation process. The dataset consists of 780 ultrasound images classified as benign, malignant, and normal, with data augmentation techniques applied for improved model generalization. The evaluation metrics such as precision, recall, F1-score and Dice similarity are used to evaluate the model. The proposed model outperforms the conventional and state-of-art models for segmentation performance.
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