Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image
IntroductionUncorrected refractive errors are a leading cause of preventable vision impairment globally, particularly affecting individuals in low-resource regions where timely diagnosis and screening access remain significant challenges despite the availability of economical treatments.AimThis stud...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Computer Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1576958/full |
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| Summary: | IntroductionUncorrected refractive errors are a leading cause of preventable vision impairment globally, particularly affecting individuals in low-resource regions where timely diagnosis and screening access remain significant challenges despite the availability of economical treatments.AimThis study introduces a novel deep learning-based system for automated refractive error classification using photorefractive images acquired via a standard smartphone camera.MethodsA multi-branch convolutional neural network (CNN) was developed and trained on a dataset of 2,139 corneal images collected from an Indonesian public eye hospital. The model was designed to classify refractive errors into four categories: significant myopia, significant hypermetropia, insignificant refractive error, and not applicable to classified. Grad-CAM visualization was employed to provide insights into the model’s interpretability.ResultsThe 3-branch CNN architecture demonstrated superior performance, achieving an overall test accuracy of 91%, precision of 96%, and recall of 98%, with an area under the curve (AUC) score of 0.9896. Its multi-scale feature extraction pathways were pivotal in effectively addressing overlapping red reflex patterns and subtle variations between classes.ConclusionThis study establishes the feasibility of smartphone-based photorefractive assessment integrated with artificial intelligence for scalable and cost-effective vision screening. By training the CNN model with a real-world dataset representative of Southeast Asian populations, this system offers a reliable solution for early refractive error detection with significant implications for improving accessibility to eye care services in resource-limited settings. |
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| ISSN: | 2624-9898 |