基于AlexNet和集成分类器的乳腺癌计算机辅助诊断方法

为解决在计算机辅助诊断(computer aided diagnosis, CAD)中采用人工提取医学影像特征的弊端,在ImageNet数据集上预训练深度神经网络模型Alexnet,通过迁移学习再训练后的Alexnet模型对医学影像进行特征提取,利用集成学习方法训练分类器进行分类。试验结果表明,基于Alexnet和随机森林方法的分类器正确率达到了0.87±0.03,集成分类器的分类性能优于单一分类器。

Computer aided diagnosis method for breast cancer based on AlexNet and ensemble classifiers

In order to solve the manual feature extraction of medical images in computer aided diagnosis, Alexnet was pre-trained on the ImageNet dataset, and feature extraction was performed on the medical image based on Alexnet with transfer learning. The ensemble learning method was used to train the classifier to classify and obtain a better classification effect than the single classifier. The results showed that the AUC(area under curve) of Alexnet deep learning model and random forest ensemble classifier reached 0.87±0.03, and the effect of the ensemble classifier was better than that of the single classifier in the same network depth.