Breast Cancer Image Classification Method Based on Deep Transfer Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912021802385408 |
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| author | Wang, Weimin Li, Yufeng Yan, Xu Xiao, Mingxuan Gao, Min |
| author_facet | Wang, Weimin Li, Yufeng Yan, Xu Xiao, Mingxuan Gao, Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09226 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Breast Cancer Image Classification Method Based on Deep Transfer Learning Wang, Weimin Li, Yufeng Yan, Xu Xiao, Mingxuan Gao, Min Image and Video Processing Computer Vision and Pattern Recognition Machine 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. |
| title | Breast Cancer Image Classification Method Based on Deep Transfer Learning |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.09226 |