Department of Software, Korea National University of Transportation, Chungju 27469, Korea.
Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea.
Sensors (Basel). 2021 Mar 22;21(6):2222. doi: 10.3390/s21062222.
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
脑肿瘤分类在临床诊断和有效治疗中起着重要作用。在这项工作中，我们提出了一种使用深度特征集成和机器学习分类器进行脑肿瘤分类的方法。在我们提出的框架中，我们采用迁移学习的概念，并使用几个预训练的深度卷积神经网络从脑磁共振（MR）图像中提取深度特征。然后，由几个机器学习分类器评估提取的深度特征。选择在几个机器学习分类器上表现良好的前三个深度特征，并将其连接起来作为深度特征集，然后将其输入到几个机器学习分类器中以预测最终输出。为了评估不同类型的预训练模型作为深度特征提取器、机器学习分类器以及深度特征集在脑肿瘤分类中的有效性，我们使用了三个可从网上公开获取的不同脑磁共振成像（MRI）数据集。实验结果表明，深度特征集可以显著提高性能，在大多数情况下，支持向量机（SVM）与径向基函数（RBF）核的性能优于其他机器学习分类器，特别是对于大型数据集。
