School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China.
Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun, 558000, Guizhou, China.
Sci Rep. 2024 Oct 10;14(1):23758. doi: 10.1038/s41598-024-74025-z.
Early screening of breast cancer through image recognition technology can significantly increase the survival rate of patients. Therefore, breast cancer pathological image is of great significance for medical diagnosis and clinical research. In recent years, numerous deep learning models have been applied to breast cancer image classification, with deep CNN being a typical representative. Due to the use of multi-depth small convolutional kernels in mainstream CNN architectures such as VGG and Inception, the obtained image features often have high dimensionality. Although high dimensionality can bring more fine-grained features, it also increases the computational complexity of subsequent classifiers and may even lead to the curse of dimensionality and overfitting. To address these issues, a novel embedded kernel CNN principal component feature fusion (CNN-PCFF) algorithm is proposed. The constructed kernel function is embedded in the principal component analysis to form the multi-kernel principal component. Multi-kernel principal component analysis is used to fuse the high dimensional features obtained from the convolution base into some representative comprehensive variables, which are called kernel principal components, so as to achieve the purpose of dimensionality reduction. Any type of classifier can be added based on multi-kernel principal components. Through experimental analysis on two public breast cancer image datasets, the results show that the proposed algorithm can improve the performance of the current mainstream CNN architecture and subsequent classifiers. Therefore, the proposed algorithm in this paper is an effective tool for the classification of breast cancer pathological images.
通过图像识别技术进行早期乳腺癌筛查可以显著提高患者的生存率。因此，乳腺癌病理图像对于医学诊断和临床研究具有重要意义。近年来，许多深度学习模型已被应用于乳腺癌图像分类，深度卷积神经网络（CNN）是一个典型的代表。由于主流 CNN 架构（如 VGG 和 Inception）中使用了多深度小卷积核，因此获得的图像特征通常具有较高的维度。虽然高维度可以带来更细粒度的特征，但它也增加了后续分类器的计算复杂度，甚至可能导致维度灾难和过拟合。针对这些问题，提出了一种新的嵌入式核 CNN 主成分特征融合（CNN-PCFF）算法。所构建的核函数被嵌入到主成分分析中，形成多核主成分。多核主成分分析用于融合卷积基中获得的高维特征，形成一些具有代表性的综合变量，称为核主成分，从而达到降维的目的。可以基于多核主成分添加任何类型的分类器。通过对两个公共乳腺癌图像数据集的实验分析，结果表明，所提出的算法可以提高当前主流 CNN 架构和后续分类器的性能。因此，本文提出的算法是乳腺癌病理图像分类的有效工具。
