Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI

The article introduces a novel deep learning model, Residual-SwinCA-Net, for segmenting malignant lesions in Breast Ultrasound (BUSI) images. The model integrates Convolutional Neural Networks (CNNs) and Swin Transformers, incorporating channel-aware mechanisms and residual connections. The focus is on medical image analysis, specifically lesion segmentation, which is a critical task in medical diagnosis. The use of ArXiv as the source indicates this is a pre-print research paper, suggesting the work is preliminary and hasn't undergone peer review yet.

• •The research focuses on a specific medical imaging task: malignant lesion segmentation.
• •The proposed model, Residual-SwinCA-Net, combines CNNs and Swin Transformers.
• •The source is ArXiv, indicating a pre-print research paper.