To improve the state-of-the-art works in breast cancer diagnosis, this paper proposes ensemble-based deep-transfer learning with classifiers namely optimizable K-nearest neighbors, optimizable naïve Bayes, optimizable ensemble, and optimizable support vector machine algorithms for the auto-feature extraction, detection, and severity classification of input mammograms. The hyperparameters of these classification algorithms are optimized by using the Bayesian optimization technique. The extracted robust features are normalized and then classified using the Bayesian optimized classifiers. Thus, the work throws a light of research on detecting whether the input mammogram is normal or abnormal. Afterward, it further focuses on the severity classification of abnormalities that is, benign or malignant. The aforesaid algorithms are trained and tested for the three-class classification problem, thus achieving a maximum performance using the Bayesian optimized SVM algorithm applied with ResNet18 deep features providing classification accuracy of 99.689% for MIAS and 98.883% for INbreast dataset.

中文翻译：

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用于乳腺癌诊断的贝叶斯优化分类器的深层特征

为了改进乳腺癌诊断中的最新工作，本文提出了基于集成的深度迁移学习与分类器，即可优化的 K-最近邻、可优化的朴素贝叶斯、可优化的集成和可优化的支持向量机算法，用于输入乳房 X 光照片的自动特征提取、检测和严重性分类。这些分类算法的超参数使用贝叶斯优化技术进行优化。提取的鲁棒特征被归一化，然后使用贝叶斯优化分类器进行分类。因此，这项工作对检测输入的乳房 X 光照片是正常还是异常进行了研究。之后，它进一步关注异常的严重程度分类，即良性或恶性。