Document detail
ID
oai:arXiv.org:2404.09226
Topic
Electrical Engineering and Systems... Computer Science - Computer Vision... Computer Science - Machine Learnin...Author
Wang, Weimin Li, Yufeng Yan, Xu Xiao, Mingxuan Gao, MinCategory
Computer Science
Year
2024
listing date
9/18/2024
Keywords
science transfer deep algorithm computer imageAbstract
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.
;Comment: 12 pages, 8 figures, 2024 International Conference on Image Processing, Machine Learning and Pattern Recognition
Wang, Weimin,Li, Yufeng,Yan, Xu,Xiao, Mingxuan,Gao, Min, 2024, Breast Cancer Image Classification Method Based on Deep Transfer Learning
Assessing the Equivalence of Brain-Derived Measures from Two 3D T1-Weighted Acquisitions: One Covering the Brain and One Covering the Brain and Spinal Cord
>0 t1-weighted brain icc cns-t1 measures spinal
