Breast Cancer Image Classification Method Based on Deep Transfer Learning
This work addresses breast cancer detection for medical applications, but it is incremental as it builds on existing deep learning and transfer learning methods.
The paper tackled the problem of limited samples and low accuracy in breast cancer pathological image classification by proposing a deep transfer learning model with attention mechanisms, achieving over 84.0% efficiency on the test set.
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.