摘要
目前基于卷积神经网络(CNN)的视网膜光学相干层析成像(OCT)图像分类方法存在对于小范围病变区域识别不清的问题,导致在判断年龄相关性黄斑变性(AMD)疾病干湿性、脉络膜新生血管形成(CNV)的活动性时准确率不高,而正确判断病变类型对于眼科医生制定治疗方案至关重要。为此本文提出了一种基于自注意力机制的CNN模型MobileX-ViT,将传统卷积层和自注意力模块结合,同时提取浅层网络的特征信息并获取图像的全局信息,以提高模型分类准确率。实验证明,相比于经典CNN分类模型Inception-V3、ResNet-50、VGG-16和MobileNeXt,文章提出模型在分类准确率上分别提高了5.6%、5.3%、4.5%和2.8%,证明了模型的有效性,为解决目前视网膜OCT图像分类中对于小范围病变区域识别不清的问题提供了新的方法。
At present,the retinal optical coherence tomography(OCT)image classification method based on convolutional neural network(CNN)has the problem of unclear identification of small-scale lesion areas,which leads to the difficulty in diagnosing the dry and wet aspects of age-related macular degeneration(AMD),and judging the activity of choroidal neovascularization(CNV),but correct judgment of lesion type is crucial for ophthalmologists to formulate treatment plans.Therefore,a CNN model MobileX-ViT based on the self-attention mechanism is proposed,which combines the traditional convolution layers and self-attention module,and simultaneously extracts the feature information of the shallow network and obtains the global information of the image to improve the performance of the model.Experiments have proved that compared with the classic CNN classification models Inception-V3,ResNet-50,VGG-16 and MobileNeXt,the classification accuracy of the proposed model is increased by 5.6%,5.3%,4.5%and 2.8%respectively.The effectiveness of the model is proved,and it provides a new method to solve the problem of unclear identification of small-scale lesion areas in the current classification of retinal OCT images.
作者
杨文逸
陈明惠
吴玉全
秦楷博
杨政奇
YANG Wenyi;CHEN Minghui;WU Yuquan;QIN Kaibo;YANG Zhengqi(Shanghai Engineering Research Center for Interventional Medical Devices,School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《光学技术》
CAS
CSCD
北大核心
2024年第1期112-119,共8页
Optical Technique
基金
上海市科委产学研医项目(15DZ1940400)。
