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.
Abstract (translated)
为解决有限样本量、耗时特征设计和乳腺癌图像检测和分类准确度低的问题，我们提出了一个结合深度学习和迁移学习思想的乳腺癌图像分类模型算法。该算法基于深度神经网络的DenseNet结构，通过引入注意机制构建了一个网络模型，并使用多级迁移学习对增强数据集进行训练。实验结果表明，该算法在测试集上的效率超过84.0%，与之前模型的分类准确度相比有显著提高，使得该算法适用于医疗乳腺癌检测任务。
URL
https://arxiv.org/abs/2404.09226
