DeepEyeNet: Adaptive Genetic Bayesian Algorithm Based Hybrid ConvNeXtTiny Framework For Multi-Feature Glaucoma Eye Diagnosis

2025-01-19

AI Summary

This study addresses the clinical need for early glaucoma screening by proposing a fully automated diagnostic system based on fundus images. Methodologically, it employs dynamic-threshold normalization to enhance image robustness; leverages a U-Net architecture for precise optic cup and optic disc segmentation; introduces a dual-path feature fusion mechanism integrating anatomical structure and texture features; and develops a lightweight Hybrid ConvNeXtTiny classification framework optimized via a novel Adaptive Genetic Bayesian Optimization (AGBO) algorithm for joint hyperparameter tuning. Evaluated on the EyePACS-AIROGS-light-V2 dataset, the system achieves 95.84% classification accuracy, outperforming state-of-the-art methods. Key contributions include: (1) the design and validation of the AGBO algorithm; (2) a multi-feature coupled diagnostic architecture unifying structural and textural cues; and (3) an end-to-end, clinically deployable lightweight implementation achieving high accuracy with low computational overhead.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Glaucoma Detection
• Retinal Imaging
• Medical Assistance System

Innovation

• DeepEyeNet
• AGBO algorithm
• advanced image processing