| JIANG Jiewei(蒋杰伟)*,ZHANG Yi*,XIE He**,GONG Jiamin*,ZHU Shaomin*,WU Shanjun** *,LI Zhongwen* **.[J].高技术通讯(英文),2023,29(4):377~387 |
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| Deep learning-based automated grading of visual impairmentin cataract patients using fundus images |
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| DOI： |
| 中文关键词: |
| 英文关键词: deep learning, convolutional neural network (CNN), visual impairment grading,
fundus image, efficient channel attention |
| 基金项目: |
| Author Name | Affiliation | | JIANG Jiewei(蒋杰伟)* | (*School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P. R. China)
(**School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325000, P. R. China)
(***Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, P. R. China) | | ZHANG Yi* | | | XIE He** | | | GONG Jiamin* | | | ZHU Shaomin* | | | WU Shanjun** * | | | LI Zhongwen* ** | |
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| 中文摘要: |
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| 英文摘要: |
| Cataract is the leading cause of visual impairment globally. The scarcity and uneven distribution
of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clinic.
In this study, a deep learning-based automated grading system of visual impairment in cataract
patients is proposed using a multi-scale efficient channel attention convolutional neural network
(MECA_CNN). First, the efficient channel attention mechanism is applied in the MECA_CNN to
extract multi-scale features of fundus images, which can effectively focus on lesion-related regions.
Then, the asymmetric convolutional modules are embedded in the residual unit to reduce the information
loss of fine-grained features in fundus images. In addition, the asymmetric loss function is
applied to address the problem of a higher false-negative rate and weak generalization ability caused
by the imbalanced dataset. A total of 7 299 fundus images derived from two clinical centers are employed
to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by
cataract (MVICC), moderate to severe visual impairment caused by cataract (MSVICC), and normal
sample. The experimental results demonstrate that the MECA_CNN provides clinically meaningful
performance for visual impairment grading in the internal test dataset: MVICC (accuracy, sensitivity,
and specificity; 91. 3%, 89. 9%, and 92%), MSVICC (93. 2%, 78. 5%, and 96. 7%),
and normal sample (98. 1%, 98. 0%, and 98. 1%). The comparable performance in the external
test dataset is achieved, further verifying the effectiveness and generalizability of the MECA_CNN
model. This study provides a deep learning-based practical system for the automated grading of visual
impairment in cataract patients, facilitating the formulation of treatment strategies in a timely manner
and improving patients’ vision prognosis. |
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