Yang Yunfeng, Wang Jiaqi
Department of Mathematics and Statistics, Northeast Petroleum University, Daqing, China.
J Xray Sci Technol. 2024;32(3):677-687. doi: 10.3233/XST-230296.
Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional neural network which helps to realize the automatic recognition and classification of pathological images. In this paper, a novel method based on deep learning and wavelet transform is proposed to classify the pathological images of breast cancer. Firstly, the image flip technique is used to expand the data set, then the two-level wavelet decomposition and reconfiguration technology is used to sharpen and enhance the pathological images. Secondly, the processed data set is divided into the training set and the test set according to 8:2 and 7:3, and the YOLOv8 network model is selected to perform the eight classification tasks of breast cancer pathological images. Finally, the classification accuracy of the proposed method is compared with the classification accuracy obtained by YOLOv8 for the original BreaKHis dataset, and it is found that the algorithm can improve the classification accuracy of images with different magnifications, which proves the effectiveness of combining two-level wavelet decomposition and reconfiguration with YOLOv8 network model.
乳腺癌是全球发病率和死亡率较高的癌症之一，严重威胁着女性的健康。随着深度学习的发展，人们对计算机辅助诊断技术的认可度越来越高。传统的数据特征提取技术已逐渐被基于卷积神经网络的特征提取技术所取代，这有助于实现病理图像的自动识别和分类。本文提出了一种基于深度学习和小波变换的新方法来对乳腺癌病理图像进行分类。首先，利用图像翻转技术扩充数据集，然后采用二级小波分解和重构技术对病理图像进行锐化和增强。其次，将处理后的数据集按照8:2和7:3划分为训练集和测试集，并选用YOLOv8网络模型对乳腺癌病理图像进行八分类任务。最后，将该方法的分类准确率与YOLOv8对原始BreaKHis数据集所获得的分类准确率进行比较，发现该算法能够提高不同放大倍数图像的分类准确率，证明了二级小波分解和重构与YOLOv8网络模型相结合的有效性。
