摘要

针对光学相干层析视网膜图像进行人工分类诊断时存在漏检、效率低等问题,提出一种基于深度学习技术构建联合多层特征的卷积神经网络分类算法。首先通过均值漂移和数据归一化算法对视网膜图像进行预处理,并结合损失函数加权算法解决数据不平衡问题;其次使用轻量深度可分离卷积替代普通卷积层,降低模型参数量,采用全局平均池化替换全连接层,增加空间鲁棒性,并联合不同卷积层构建特征融合层,加强层间特征流通;最后使用SoftMax分类器进行图像分类。实验结果表明,该模型在准确率、精确率、召回率上分别达到97%、95%、97%,缩短了识别时长,所提方法在视网膜图像分类诊断中具有良好的性能。

Abstract

Aiming at the problems of missed detection and low efficiency in manual classification and diagnosis of optical coherence tomography retina images, a deep learning-based convolutional network classification algorithm is proposed to construct joint multilayer features. First, retinal images are preprocessed using the mean shift and data normalization algorithm. The loss function weighting algorithm is combined to solve the data imbalance problem. Second, a lightweight deep separable convolution rather than an ordinary convolution layer is used to reduce the number of model parameters. Global average pooling replaces fully connected layers to increase spatial robustness, and different convolutional layers are used to build feature fusion layers to enhance feature circulation between layers. Finally, the SoftMax classifier is used for image classification. Experimental results show that the model can achieve 97%, 95%, and 97% in accuracy, precision, and recall, respectively, thereby reducing the recognition time. The proposed deep learning feature fusion-based method performs well in the classification and diagnosis of retinal images.

补充资料

中图分类号：TP391.4

DOI：10.3788/LOP57.241025

所属栏目：图像处理

基金项目：国家自然科学基金、福建省科技计划项目;

收稿日期：2020-04-24

修改稿日期：2020-06-09

网络出版日期：2020-12-01

作者单位    点击查看

联系人作者：钟舜聪(zhongshuncong@hotmail.com)

备注：国家自然科学基金、福建省科技计划项目;

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引用该论文

Zhang Tianfu,Zhong Shuncong,Lian Chaoming,Zhou Ning,Xie Maosong. Deep Learning Feature Fusion-Based Retina Image Classification[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241025

张添福,钟舜聪,连超铭,周宁,谢茂松. 基于深度学习特征融合的视网膜图像分类[J]. 激光与光电子学进展, 2020, 57(24): 241025