Histopathology is considered as the gold standard for diagnosing breast cancer. Traditional machine learning (ML) algorithm provides a promising performance for cancer diagnosis if the training dataset is balanced. Nevertheless, if the training dataset is imbalanced the performance of the ML model is skewed toward the majority class. It may pose a problem for the pathologist because if the benign sample is misclassified as malignant, then a pathologist could make a misjudgment about the diagnosis. A limited investigation has been done in literature for solving the class imbalance problem in computer‐aided diagnosis (CAD) of breast cancer using histopathology. This work proposes a hybrid ML model to solve the class imbalance problem. The proposed model employs pretrained ResNet50 and the kernelized weighted extreme learning machine for CAD of breast cancer using histopathology. The breast cancer histopathological images are obtained from publicly available BreakHis and BisQue datasets. The proposed method achieved a reasonable performance for the classification of the minority as well as the majority class instances. In comparison, the proposed approach outperforms the state‐of‐the‐art ML models implemented in previous studies using the same training‐testing folds of the publicly accessible BreakHis dataset.

中文翻译：

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基于核加权加权学习机的乳腺癌组织病理学图像分类

组织病理学被认为是诊断乳腺癌的金标准。如果训练数据集平衡，则传统的机器学习（ML）算法可为癌症诊断提供有希望的性能。但是，如果训练数据集不平衡，则ML模型的性能将偏向多数班。这可能对病理学家造成问题，因为如果良性样品被误分类为恶性，则病理学家可能会对诊断做出错误判断。对于使用组织病理学解决乳腺癌的计算机辅助诊断（CAD）中的类别失衡问题，文献进行了有限的研究。这项工作提出了一种混合ML模型来解决类不平衡问题。所提出的模型采用预训练的ResNet50和带核的加权极限学习机进行组织病理学CAD。乳腺癌组织病理学图像是从可公开获得的BreakHis和BisQue数据集中获得的。所提出的方法在少数族裔和多数族实例的分类上取得了合理的性能。相比之下，使用公开的BreakHis数据集的相同训练测试倍数，所提出的方法要优于先前研究中实施的最新ML模型。