论文标题
多类医学数据分类中的卷积神经网络
Convolutional Neural Networks in Multi-Class Classification of Medical Data
论文作者
论文摘要
我们报告了卷积神经网络（CNN）对大型医疗数据集的多分类分类的应用。我们详细讨论了CNN模型中的变化和数据预处理如何影响分类结果。最后，我们介绍了一个由深度学习（CNN）和浅学习模型（梯度提升）组成的合奏模型。该方法达到了64.93的精度，这是我们在本研究中获得的最高三类分类精度。我们的结果还表明，CNN和整体始终获得比精度更高的召回。最高的召回率为68.87，而最高的精度是65.04。
We report applications of Convolutional Neural Networks (CNN) to multi-classification classification of a large medical data set. We discuss in detail how changes in the CNN model and the data pre-processing impact the classification results. In the end, we introduce an ensemble model that consists of both deep learning (CNN) and shallow learning models (Gradient Boosting). The method achieves Accuracy of 64.93, the highest three-class classification accuracy we achieved in this study. Our results also show that CNN and the ensemble consistently obtain a higher Recall than Precision. The highest Recall is 68.87, whereas the highest Precision is 65.04.
