[1]HAN Lu,BI Xiaojun.Retinal optical coherence tomography image classification based on multiscale feature fusion[J].CAAI Transactions on Intelligent Systems,2022,17(2):360-367.[doi:10.11992/tis.202111024]
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Retinal optical coherence tomography image classification based on multiscale feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 2 Page number: 360-367 Column: 学术论文—智能系统 Public date: 2022-03-05
Title:
Retinal optical coherence tomography image classification based on multiscale feature fusion
Author(s):
HAN Lu1;  BI Xiaojun2
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. School of Information Engineering, Minzu University of China, Beijing 100081, China
Keywords:
retina; optical coherence tomography; attention mechanism; atrous spatial pyramid pooling; neural network; image classification; deep learning; medical image
CLC:
TP391.7
DOI:
10.11992/tis.202111024
Abstract:
The retinal optical coherence tomography (OCT) image classification method based on deep learning has problems such as low ability of network feature extraction and difficult classification of small target lesions. Therefore, this paper proposes a dual branch multiscale feature fusion network. The gating attention mechanism is added to the vgg16 network, and the deep features are transmitted to the shallow features as gating signals. The redundant features are removed more fine-grained abstract information is obtained. Simultaneously, an atrous spatial pyramid pooling (ASPP) module is introduced to increase the receptive field and capture the global context information in various proportions without reducing the feature map resolution. The ASPP module increases the classification accuracy of small target lesions. The experimental results show that the proposed method has achieved good results in the retinal OCT image classification task, and the classification accuracy has reached 97.9%.
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