DeepEyeNet: Adaptive Genetic Bayesian Algorithm Based Hybrid ConvNeXtTiny Framework For Multi-Feature Glaucoma Eye Diagnosis
This work addresses early glaucoma detection for clinical applications, but it appears incremental as it combines existing techniques like U-Net and ConvNeXtTiny with a new optimization method.
The paper tackled automated glaucoma detection from retinal fundus images by integrating image standardization, segmentation, feature extraction, and a ConvNeXtTiny classifier optimized with a novel Adaptive Genetic Bayesian Optimization algorithm, achieving a classification accuracy of 95.84% on the EyePACS-AIROGS-light-V2 dataset.
Glaucoma is a leading cause of irreversible blindness worldwide, emphasizing the critical need for early detection and intervention. In this paper, we present DeepEyeNet, a novel and comprehensive framework for automated glaucoma detection using retinal fundus images. Our approach integrates advanced image standardization through dynamic thresholding, precise optic disc and cup segmentation via a U-Net model, and comprehensive feature extraction encompassing anatomical and texture-based features. We employ a customized ConvNeXtTiny based Convolutional Neural Network (CNN) classifier, optimized using our Adaptive Genetic Bayesian Optimization (AGBO) algorithm. This proposed AGBO algorithm balances exploration and exploitation in hyperparameter tuning, leading to significant performance improvements. Experimental results on the EyePACS-AIROGS-light-V2 dataset demonstrate that DeepEyeNet achieves a high classification accuracy of 95.84%, which was possible due to the effective optimization provided by the novel AGBO algorithm, outperforming existing methods. The integration of sophisticated image processing techniques, deep learning, and optimized hyperparameter tuning through our proposed AGBO algorithm positions DeepEyeNet as a promising tool for early glaucoma detection in clinical settings.