Breast Cancer Image Classification Method Based on Deep Transfer Learning

• URL: http://arxiv.org/abs/2404.09226v2
• Date: Wed, 11 Sep 2024 07:44:21 GMT
• Title: Breast Cancer Image Classification Method Based on Deep Transfer Learning
• Authors: Weimin Wang, Yufeng Li, Xu Yan, Mingxuan Xiao, Min Gao,
• Abstract summary: A breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. 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.
• Score: 40.392772795903795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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