计算机科学
光学相干层析成像
人工智能
卷积神经网络
模式识别(心理学)
变压器
加权
特征提取
利用
计算机视觉
计算机安全
放射科
量子力学
电压
眼科
医学
物理
作者
Zongqing Ma,Qiaoxue Xie,Pinxue Xie,Fan Fan,Xinxiao Gao,Jiang Zhu
出处
期刊:Biosensors
[MDPI AG]
日期:2022-07-20
卷期号:12 (7): 542-542
被引量:16
摘要
Automatic and accurate optical coherence tomography (OCT) image classification is of great significance to computer-assisted diagnosis of retinal disease. In this study, we propose a hybrid ConvNet-Transformer network (HCTNet) and verify the feasibility of a Transformer-based method for retinal OCT image classification. The HCTNet first utilizes a low-level feature extraction module based on the residual dense block to generate low-level features for facilitating the network training. Then, two parallel branches of the Transformer and the ConvNet are designed to exploit the global and local context of the OCT images. Finally, a feature fusion module based on an adaptive re-weighting mechanism is employed to combine the extracted global and local features for predicting the category of OCT images in the testing datasets. The HCTNet combines the advantage of the convolutional neural network in extracting local features and the advantage of the vision Transformer in establishing long-range dependencies. A verification on two public retinal OCT datasets shows that our HCTNet method achieves an overall accuracy of 91.56% and 86.18%, respectively, outperforming the pure ViT and several ConvNet-based classification methods.
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