GlaLSTM: A Concurrent LSTM Stream Framework for Glaucoma Detection via Biomarker Mining
This work addresses glaucoma detection for medical professionals by providing a more interpretable model, though it appears incremental as it builds on existing LSTM and biomarker analysis methods.
The paper tackled glaucoma detection by proposing GlaLSTM, a concurrent LSTM stream framework that mines biomarker relationships, resulting in improved accuracy and enhanced interpretability for clinicians.
Glaucoma is a complex group of eye diseases marked by optic nerve damage, commonly linked to elevated intraocular pressure and biomarkers like retinal nerve fiber layer thickness. Understanding how these biomarkers interact is crucial for unraveling glaucoma's underlying mechanisms. In this paper, we propose GlaLSTM, a novel concurrent LSTM stream framework for glaucoma detection, leveraging latent biomarker relationships. Unlike traditional CNN-based models that primarily detect glaucoma from images, GlaLSTM provides deeper interpretability, revealing the key contributing factors and enhancing model transparency. This approach not only improves detection accuracy but also empowers clinicians with actionable insights, facilitating more informed decision-making. Experimental evaluations confirm that GlaLSTM surpasses existing state-of-the-art methods, demonstrating its potential for both advanced biomarker analysis and reliable glaucoma detection.