A study on the detection of breast lumps based on attentional mechanisms
Lanfeng Zhou, Zhikun Chen · 2023
Traditional networks cannot focus on which features are important when extracting features and use the same weighting for all features, so this paper investigates breast lump detection based on attention mechanisms. By adding three attention mechanisms, SE, ECA and CBAM, respectively, to the backbone network of YOLOv5s, the network's ability to extract breast lump features is enhanced for breast lump recognition. The experimental results show that adding SE or ECA or CBAM attention mechanisms alone to the YOLOv5s network, they are higher in accuracy, recall and [email protected] than the original YOLOv5s network model, and the model with the addition of CBAM attention mechanism improves the accuracy, recall and [email protected] by 3.1%, 2.6% and 3.1% respectively, which is better than the models with the addition of SE and ECA attention mechanisms. The proposed attention mechanism-based breast lump detection model can effectively detect breast lumps and provide help for early screening.