Medical & Biological Engineering & Computing ( IF 2.6 ) Pub Date : 2021-07-24 , DOI: 10.1007/s11517-021-02403-0 M A Aswathy 1 , M Jagannath 1
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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.
Graphical abstract
中文翻译：
基于集成特征的 H&E 染色组织病理学图像对乳腺癌分类的 SVM 方法
乳腺癌是全球女性死亡的最常见原因之一。如今，由于乳腺癌的致命性，医疗信息学主要关注乳腺癌图像的分类。观察者之间和观察者内部的变异性可能会导致癌症检测中的误诊。本研究提出了一种基于集成特征（纹理、几何和颜色）的支持向量机（SVM）分类器自动乳腺癌分类系统。使用了可在公共领域获得的加州大学圣巴巴拉分校 (UCSB) 数据集和 BreakHis 数据集。一个涉及 SVM 的分类比较模块，k -最近邻（k-NN)、随机森林 (RF) 和人工神经网络 (ANN) 也被提出来确定最适合从组织病理学图像中检测乳腺癌应用的分类器。这些分类器的性能根据准确度、特异性、灵敏度、平衡准确度和F分数等指标进行分析。结果表明，在分类器中，SVM 分类器表现更好，在两个数据集上的测试准确率都约为 90%。此外，还针对其他分类器模型对所提出的集成 SVM 模型的重要性进行了统计分析。
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