IEEE Access | 卷:10 |
Classification of Diabetic Retinopathy Severity Based on GCA Attention Mechanism | |
Binhua Yang1  Haidi Xie1  Tongyan Li1  Yulin Liao1  Yi-Ping Phoebe Chen2  | |
[1] College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China; | |
[2] Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia; | |
关键词: Attention mechanism; convolutional neural network; deep learning; diabetic retinopathy; medical images; | |
DOI : 10.1109/ACCESS.2021.3139129 | |
来源: DOAJ |
【 摘 要 】
Diabetic retinopathy (DR) is one of the major complications caused by diabetes and can lead to severe vision loss or even complete blindness if not diagnosed and treated in a timely manner. In this paper, a new feature map global channel attention mechanism (GCA) is proposed to solve the problem of the early detection of DR. In the GCA module, an adaptive one-dimensional convolution kernel size algorithm based on the dimension of the feature map is proposed and a deep convolutional neural network model for DR color medical image severity diagnosis named GCA-EfficientNet (GENet) is designed. The training process uses transfer learning techniques with a cosine annealing learning rate adjustment strategy. The image regions of interest of GENet are visualized using a heat map. The final accuracy, precision, sensitivity and specificity of the DR dataset of the Kaggle competition reached 0.956, 0.956, 0.956, and 0.989, respectively. A large number of experiment results show that GENet based on the GCA attention mechanism can more effectively extract lesion features and classify the severity of DR.
【 授权许可】
Unknown