基于卷积神经网络的眼科光学相干断层成像图像的自动分类

Automatic classification of ophthalmic optical coherence tomography images based on the convolution neural network

目的提出一种基于卷积神经网络(convolutional neural network,CNN)的眼科光学相干断层成像(optical coherence tomography,OCT)图像自动分类方法,实现对视网膜OCT图像的自动分类,缓解人工诊断依赖医生的临床经验、费时费力等问题。方法基于公开的数据集2014_BOE_Srinivasan构建了2个样本数据集。其中样本数据集一为仅对数据集中的图像进行预处理后裁剪,样本数据集二为对取出测试集后剩余图像的裁剪过程中引入随机平移和水平翻转技术对图像进行扩充,并划分为训练集和验证集。搭建基于CNN的视网膜OCT图像分类网络,并分别使用两个数据集训练网络得到分类模型。最后使用独立的测试集对模型进行测试,并通过输出混淆矩阵查看模型对3种类别图像的分类情况。结果通过混淆矩阵计算得出,使用扩充后的图像训练的分类模型的准确度为93.43%,灵敏度为91.38%,特异度为95.88%。结论提出的基于CNN的视网膜OCT图像自动分类方法可以对老年性黄斑变性、糖尿病性黄斑水肿和正常3种类别的视网膜OCT图像进行分类。同时,数据扩充有助于提高分类算法的性能。

Objective To propose an automatic classification method of ophthalmic optical coherence tomography(OCT)images based on the convolutional neural network(CNN),and alleviate the problems of artificial diagnosis relying on the clinical experience of ophthalmologists.Methods The two sample data sets were constructed based on the OCT data set of 2014_BOE_Srinivasan.Among them,the sample data set 1 was to crop the images in the data set after preprocessing,the sample data set 2 was to augment the images by introducing the random translation and horizontal flipping technology in the images cropping of remaining images after taking out the test set,and it was divided into training set and validation set.Then a classification network based on the CNN was constructed and respectively trained by the two sample data sets to acquire the classification models.Finally,the model was tested by using the test set,and the accuracy of the model for the three types of retinal OCT images were calculated through the output confusion matrix.Results The accuracy,the sensitivity and the specificity of the model trained by augmented images were 93.43%,91.38%and 95.88%,respectively.Conclusions The proposed automatic classification method of retinal OCT images based on the CNN can classify the retinal OCT images of age-related macular degeneration,diabetic macular edema and normal.Meanwhile,data augmentation can improve the performance of classification algorithm.