Biopsy is one of the most commonly used modality to identify breast cancer in women, where tissue is removed and studied by the pathologist under the microscope to look for abnormalities in tissue. This technique can be time‐consuming, error‐prone, and provides variable results depending on the expertise level of the pathologist. An automated and efficient approach not only aids in the diagnosis of breast cancer but also reduces human effort. In this paper, we develop an automated approach for the diagnosis of breast cancer tumors using histopathological images. In the proposed approach, we design a residual learning‐based 152‐layered convolutional neural network, named as ResHist for breast cancer histopathological image classification. ResHist model learns rich and discriminative features from the histopathological images and classifies histopathological images into benign and malignant classes. In addition, to enhance the performance of the developed model, we design a data augmentation technique, which is based on stain normalization, image patches generation, and affine transformation. The performance of the proposed approach is evaluated on publicly available BreaKHis dataset. The proposed ResHist model achieves an accuracy of 84.34% and an F1‐score of 90.49% for the classification of histopathological images. Also, this approach achieves an accuracy of 92.52% and F1‐score of 93.45% when data augmentation is employed. The proposed approach outperforms the existing methodologies in the classification of benign and malignant histopathological images. Furthermore, our experimental results demonstrate the superiority of our approach over the pre‐trained networks, namely AlexNet, VGG16, VGG19, GoogleNet, Inception‐v3, ResNet50, and ResNet152 for the classification of histopathological images.

中文翻译：

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基于残差学习的CNN用于乳腺癌组织病理学图像分类

活组织检查是识别女性乳腺癌的最常用方法之一，切除组织并由病理学家在显微镜下进行研究以寻找组织异常。此技术可能很耗时，容易出错，并且会根据病理学家的专业水平提供可变的结果。自动化且有效的方法不仅有助于诊断乳腺癌，而且可以减少人工。在本文中，我们开发了一种使用组织病理学图像诊断乳腺癌肿瘤的自动化方法。在提出的方法中，我们设计了一个基于残差学习的152层卷积神经网络，命名为ResHist，用于乳腺癌组织病理学图像分类。ResHist模型从组织病理学图像中学习丰富而有区别的特征，并将组织病理学图像分为良性和恶性两类。此外，为了增强开发模型的性能，我们设计了一种基于污点归一化，图像斑块生成和仿射变换的数据增强技术。在公开的BreaKHis数据集上评估了该方法的性能。所提出的ResHist模型对组织病理学图像的分类可达到84.34％的准确度和90.49％的F1评分。同样，当采用数据增强时，此方法可实现92.52％的准确性和93.45％的F1得分。所提出的方法在良性和恶性组织病理学图像的分类方面优于现有方法。此外，