Domain-specific multi-transfer approaches for deep learning-based glaucoma screening in high myopia patients

Abstract

[Abstract]: Glaucoma is a leading cause of irreversible blindness worldwide, with early detection being essential to preventing vision loss. However, diagnosing glaucoma in highly myopic patients poses significant challenges due to anatomical alterations, such as optic nerve head deformation and retinal nerve fiber layer thinning, potentially obscuring key disease features and causing misdiagnosis. To address these limitations, we propose a novel deep learning-based framework for automated glaucoma screening in highly myopic eyes. Our approach leverages multi-transfer learning, integrating large-scale pretraining with domain-specific adaptation using ophthalmic disease datasets. This methodology enables the model to extract robust and highly discriminative features, improving sensitivity to glaucomatous changes in myopic eyes. Additionally, we incorporate domain transfer pipeline to address the distributional differences between standard datasets and myopia-specific cases, further enhancing the model’s generalization capabilities. To rigorously evaluate our approach, we conduct a comprehensive analysis of state-of-the-art architectures and transfer learning strategies, assessing their impact on classification performance. Experimental results demonstrate that the proposed model consistently outperforms baseline methods, achieving superior accuracy and robustness in glaucoma detection within highly myopic populations. These findings underscore the potential of AI-driven screening tools as reliable and accurate diagnostic aids, supporting clinicians in the early detection and effective management of glaucoma in complex cases.