A federal learning-driven artificial intelligence framework for fundus image myopia diagnosis

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Myopia is a prevalent refractive error that can lead to severe visual damage, making early diagnosis through fundus imaging essential.
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Fundus photography is a cost-effective, non-invasive method for screening various ocular diseases, including myopia.
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Deep learning techniques have shown promise in automating the diagnosis of ophthalmic diseases, achieving accuracy comparable to ophthalmologists.
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Federated learning (FL) offers a decentralized approach to training diagnostic models while addressing data privacy concerns in healthcare.
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The proposed FL-based myopia classification framework aims to enhance model performance across different medical centers while protecting patient data.

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