A breast cancer image classification method based on deep transfer learning is proposed to address the challenges of limited samples, time-consuming feature design, and low accuracy in breast cancer pathological image classification. The method combines deep learning and transfer learning, using the DenseNet structure and integrating attention mechanisms to enhance performance. The model is trained using a multi-level transfer learning approach on an enhanced dataset. Experimental results show that the algorithm achieves over 84.0% accuracy on the test set, significantly improving classification accuracy compared to previous models, making it suitable for medical breast cancer detection tasks. Breast cancer is a major health issue for women, with high incidence and mortality rates globally. Current clinical diagnosis relies on pathologists' expertise, which is time-consuming and subjective. Deep learning, with its ability to learn features automatically, has shown promise in improving diagnostic accuracy and efficiency. However, deep learning requires large datasets, which are challenging to obtain. To address this, the study proposes a deep transfer learning method that leverages pre-trained models to improve classification accuracy. The method uses the DenseNet network, enhanced with attention mechanisms, and applies transfer learning using the ImageNet dataset and the LC2500 lung cancer dataset. The model is further fine-tuned using the BreakHis breast cancer dataset. The model's performance is evaluated using classification accuracy metrics, showing significant improvements over traditional methods. The model achieves high accuracy in classifying benign and malignant breast cancer images, with a 2-6% improvement over baseline models. The study also demonstrates that transfer learning enhances model performance by reducing overfitting and improving generalization. The model's parameters and size are slightly larger than traditional models, but the improvement in classification accuracy justifies this. The study highlights the importance of balancing accuracy and interpretability in future research. The proposed method shows promise in improving breast cancer diagnosis through deep transfer learning.A breast cancer image classification method based on deep transfer learning is proposed to address the challenges of limited samples, time-consuming feature design, and low accuracy in breast cancer pathological image classification. The method combines deep learning and transfer learning, using the DenseNet structure and integrating attention mechanisms to enhance performance. The model is trained using a multi-level transfer learning approach on an enhanced dataset. Experimental results show that the algorithm achieves over 84.0% accuracy on the test set, significantly improving classification accuracy compared to previous models, making it suitable for medical breast cancer detection tasks. Breast cancer is a major health issue for women, with high incidence and mortality rates globally. Current clinical diagnosis relies on pathologists' expertise, which is time-consuming and subjective. Deep learning, with its ability to learn features automatically, has shown promise in improving diagnostic accuracy and efficiency. However, deep learning requires large datasets, which are challenging to obtain. To address this, the study proposes a deep transfer learning method that leverages pre-trained models to improve classification accuracy. The method uses the DenseNet network, enhanced with attention mechanisms, and applies transfer learning using the ImageNet dataset and the LC2500 lung cancer dataset. The model is further fine-tuned using the BreakHis breast cancer dataset. The model's performance is evaluated using classification accuracy metrics, showing significant improvements over traditional methods. The model achieves high accuracy in classifying benign and malignant breast cancer images, with a 2-6% improvement over baseline models. The study also demonstrates that transfer learning enhances model performance by reducing overfitting and improving generalization. The model's parameters and size are slightly larger than traditional models, but the improvement in classification accuracy justifies this. The study highlights the importance of balancing accuracy and interpretability in future research. The proposed method shows promise in improving breast cancer diagnosis through deep transfer learning.