Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI
Published:Dec 9, 2025 04:52
•1 min read
•ArXiv
Analysis
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.
Key Takeaways
Reference
“The article's focus on BUSI image segmentation and the integration of CNNs and Transformers highlights a trend in medical image analysis towards more sophisticated and hybrid architectures.”