Cloud-Based RetinaNet Framework for Accurate Detection of Glaucoma in Retinal Images
Keywords:
Glaucoma detection, RetinaNet, Cloud-based framework, Retinal images, Diagnostic toolsAbstract
The Cloud-Based RetinaNet Framework seeks to provide an accurate approach for glaucoma detection in retinal pictures, improving diagnostic precision and scalability. Glaucoma, marked by gradual optic nerve deterioration, may result in permanent blindness if not identified. This system utilizes RetinaNet's focused loss technique, particularly designed to tackle class imbalance in medical datasets, hence facilitating the effective detection of glaucoma signs. The framework employs cloud-based infrastructure to provide high-performance processing of extensive retinal imaging datasets, enabling swift analysis and remote access to outcomes. The main aim is to develop a dependable, automated method proficient in identifying early-stage glaucoma, including critical indicators like optic disc cupping and retinal nerve fiber layer thinning. The objective is to enhance diagnostic efficiency, provide prompt therapies, and aid ophthalmologists in making precise evaluations, hence minimizing the risk of vision loss and enhancing patient outcomes. The suggested methodology has significant potential for revolutionizing glaucoma detection in clinical practice. Results from the RIM-ONE_DL dataset indicate that the proposed RetinaNet system attains an overall accuracy of 97.2%, with a sensitivity of 97.6% and a specificity of 96.8%.
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