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Deep learning-based automated grading of visual impairment in cataract patients using fundus images

High Technology Letters 2023年第4期29卷 377-387页

作者：蒋杰伟 ZHANG Yi XIE He GONG Jiamin ZHU Shaomin WU Shanjun LI Zhongwen School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121P.R.China School of Ophthalmology and Optometry and Eye Hospital Wenzhou Medical UniversityWenzhou 325000P.R.China Ningbo Eye Hospital Wenzhou Medical UniversityNingbo 315000P.R.China

Cataract is the leading cause of visual impairment *** scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the *** 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 ***,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus *** 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 7299 fundus images derived from two clinical centers are em-ployed 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 nor-mal *** experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,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 *** study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

关键词： deep learning convolutional neural network(CNN) visual impairment grading fundus image efficient channel attention

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