改善乳腺癌诊断分类性能的生理病理图象中降低的深层革命起动特征(R-DeCAF) (Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis)

2023 年 1 月 5 日

Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis

翻译：改善乳腺癌诊断分类性能的生理病理图象中降低的深层革命起动特征(R-DeCAF)

Bahareh Morovati,Reza Lashgari,Mojtaba Hajihasani,Hasti Shabani

Breast cancer is the second most common cancer among women worldwide. Diagnosis of breast cancer by the pathologists is a time-consuming procedure and subjective. Computer aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to a higher accuracy in the classification task and dimension reduction plays an important role. Therefore, different dimension reduction methods are applied to achieve an effective combination of features by capturing the essence of DeCAF features. To this purpose, we have proposed reduced deep convolutional activation features (R-DeCAF). In this framework, pre-trained CNNs such as AlexNet, VGG-16 and VGG-19 are utilized in transfer learning mode as feature extractors. DeCAF features are extracted from the first fully connected layer of the mentioned CNNs and support vector machine has been used for binary classification. Among linear and nonlinear dimensionality reduction algorithms, linear approaches such as principal component analysis (PCA) represent a better combination among deep features and lead to a higher accuracy in the classification task using small number of features considering specific amount of cumulative explained variance (CEV) of features. The proposed method is validated using experimental BreakHis dataset. Comprehensive results show improvement in the classification accuracy up to 4.3% with less computational time. Best achieved accuracy is 91.13% for 400x data with feature vector size (FVS) of 23 and CEV equals to 0.15 using pre-trained AlexNet as feature extractor and PCA as feature reduction algorithm.

翻译：乳腺癌是全世界妇女中第二常见的癌症。病理学家对乳腺癌的诊断是一个耗时和主观的过程。计算机辅助诊断框架通过自动对数据进行分类来减轻病理学家的工作量, 在这些数据中,深演神经网络(CNNs)是有效的解决办法。从受过训练的CNN的激活层中提取的特征被称为深演动启动功能(DeCAF )。在本文中,我们分析所有DECAF的特性并不一定导致分类任务和尺寸减少方面400个更精确的400个。因此,应用了不同尺寸的降低方法,通过采集 DeCAF特征的精髓来有效地组合各种特征。为此,我们提议降低深演动神经网络(R-DeCAFAF)的精度。在这个框架中,事先受过训练的CNNC如AlexNet、VGG-16和VGG-19等传输学习模式作为特征提取器。 DeCAFA的精度和辅助矢量计算器的第一个完全连结层层层, 用于二元值分类。在线性和非线性肝变变精度分析中, 将精度的精度的精度分析中,将精度的精度的精度和精度的精度的精度的精度分析用于精确度分析。将精度的精度的精度分析,将精度的精度的精度的精度的精度的精度的精度的精度的精度的精度和精度的精度的精度的精度的精度的精度的精度分析,将精度的精度的精度的精度的精度的精度分析,将精度的精度的精度的精度的精度比的精度比的精度比的精度分析。