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| Automatic Diagnosis of Stages 1-3 Retinopathy of Prematurity Based on Deep Convolutional Neural Network |
| Jia Liu1, Qinglan Pu1, Peng Li2, Qiaoyun Zhou1, Weixin Xu1, Yong Li1 |
| 1Department of Ophthalmology, Jiaxing Maternity and Child Health Care Hospital, Jiaxing 314000, China;
2Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314005, China |
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Abstract Objective: The aim of the system is to research the automatic diagnosis of stages 1-3 of retinopathy of prematurity(ROP) using Deep Convolutional Neural Networks(DCNN). Methods: In this retrospective study, using 12 219 retinal images of preterm infants, which collected from January 2019 to December 2020 at the Department of Ophthalmology, Jiaxing Maternity and Child Health Care Hospital, we constructed a retinal images dataset for Ophthalmology of Ophthalmology, preterm infants. Based on the segmented demarcation lines or ridge, the region of interest (ROI) were calculated, features from the ROI segmentated images were extracted and the classifier was trained using a five-fold cross-validation method to automatically diagnose stages 1-3 ROP. The performance of the DCNN and analyzed the consistency with clinical diagnosis results on the test data set was evaluated. Results: The trained system achieved an average accuracy of 98% for all the four categories. The sensitivity and specificity of the system reached 0.975 7 and 0.975 6, when diagnosing non-ROP images; 0.922 1 and 0.983 7, when diagnosing stage 1; 0.933 1 and 0.988 6, when diagnosing stage 2. At the same time, the sensitivity and specificity for the diagnosis of stage 3 ROP images were as high as 0.910 2 and 0.992 8. The Kappa value of the system for the diagnosis was 0.905 9, which was close to perfect agreement with the clinic diagnosis. Conclusion: The system based on DCNN, trained using features extracted for segmented ROI images, could diagnose automatically stages 1-3 ROP with a high accuracy.
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Received: 07 March 2022
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| Fund:Zhejiang Province Medical and Health Science and Technology General Project (2020KY965); Jiaxing Science and Technology Project(2020AD30041) |
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Corresponding Authors:
Qinglan Pu, Department of Ophthalmology, Jiaxing Maternity and Child Health Care Hospital, Jiaxing 314000, China (Email:pqlxiaolan@163.com)
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| [1] |
. [J]. Chinese Journal of Optometry Ophthalmology and Visual science, 2023, 25(8): 0-. |
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