School of Electronics Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India.
Med Biol Eng Comput. 2021 Sep;59(9):1773-1783. doi: 10.1007/s11517-021-02403-0. Epub 2021 Jul 24.
Breast cancer is one among the most frequent reasons of women's death worldwide. Nowadays, healthcare informatics is mainly focussing on the classification of breast cancer images, due to the lethal nature of this cancer. There are chances of inter- and intra-observer variability that may lead to misdiagnosis in the detection of cancer. This study proposed an automatic breast cancer classification system that uses support vector machine (SVM) classifier based on integrated features (texture, geometrical, and color). The University of California Santa Barbara (UCSB) dataset and BreakHis dataset, which are available in public domain, were used. A classification comparison module which involves SVM, k-nearest neighbor (k-NN), random forest (RF), and artificial neural network (ANN) was also proposed to determine the classifier that best suits for the application of breast cancer detection from histopathology images. The performance of these classifiers was analyzed against metrics like accuracy, specificity, sensitivity, balanced accuracy, and F-score. Results showed that among the classifiers, the SVM classifier performed better with a test accuracy of approximately 90% on both the datasets. Additionally, the significance of the proposed integrated SVM model was statistically analyzed against other classifier models.
乳腺癌是全球女性死亡的最常见原因之一。如今，医疗保健信息学主要专注于乳腺癌图像的分类，因为这种癌症具有致命性。由于存在观察者间和观察者内的变异性，因此在癌症检测中可能会导致误诊。本研究提出了一种自动乳腺癌分类系统，该系统使用支持向量机（SVM）分类器基于集成特征（纹理、几何和颜色）。使用了公共领域可用的加利福尼亚大学圣巴巴拉分校（UCSB）数据集和 BreakHis 数据集。还提出了一个分类比较模块，其中包括 SVM、k-最近邻（k-NN）、随机森林（RF）和人工神经网络（ANN），以确定最适合从组织病理学图像中检测乳腺癌的分类器。根据准确性、特异性、敏感性、平衡准确性和 F 分数等指标对这些分类器的性能进行了分析。结果表明，在这些分类器中，SVM 分类器在两个数据集上的测试准确性均约为 90%，表现更好。此外，还对所提出的集成 SVM 模型相对于其他分类器模型的统计学意义进行了分析。
