Breast Cancer Image Classification Method Based on Deep Transfer Learning
<|reference_start|>Breast Cancer Image Classification Method Based on Deep Transfer Learning: To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. This algorithm is based on the DenseNet structure of deep neural networks, and constructs a network model by introducing attention mechanisms, and trains the enhanced dataset using multi-level transfer learning. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0\% in the test set, with a significantly improved classification accuracy compared to previous models, making it applicable to medical breast cancer detection tasks.<|reference_end|>
@article{wang2024breast,
    title={Breast Cancer Image Classification Method Based on Deep Transfer
  Learning},
    author={Weimin Wang, Yufeng Li, Xu Yan, Mingxuan Xiao, Min Gao},
    journal={arXiv preprint arXiv:2404.09226},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09226},
    primaryClass={eess.IV cs.CV cs.LG}
}