用三阶段方法提升高血压视网膜病变分期诊断准确率。
Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis
- 三路深度特征融合后，用机器学习分类提高诊断精度。
- 最优方案达到94.66%准确率，超越单模型与已有研究。
- 适合医学影像智能诊断、眼科辅助决策系统研发者。
高血压视网膜病变（HR）是一种可能导致永久性视力丧失的严重眼病，早期诊断至关重要。传统方法耗时且主观性强，亟需自动化、可靠的诊断系统。现有研究多依赖单一深度学习模型，难以有效区分HR不同阶段。本文提出三阶段方法：第一阶段测试14种CNN模型，DenseNet169、MobileNet和ResNet152表现最佳，其中DenseNet169达87.73%准确率；第二阶段将三模型深度特征融合，采用机器学习算法（SVM、RF、XGBoost）分类，SVM（sigmoid核）表现最优，达92.00%准确率；第三阶段引入元启发式优化（GA、ABC、PSO、HHO）进行特征选择，HHO取得最佳效果，准确率、精确率、召回率均达94.66%，F1-score为94.64%，Cohen's Kappa达0.9286。整体方法显著优于单一CNN模型及先前研究，在诊断准确性和泛化能力上均有提升。
原文摘要 · Abstract (English)
Hypertensive retinopathy (HR) is a severe eye disease that may cause permanent vision loss if not diagnosed early. Traditional diagnostic methods are time-consuming and subjective, highlighting the need for an automated, reliable system. Existing studies often use a single Deep Learning (DL) model, struggling to distinguish HR stages. This study introduces a three-stage approach to enhance HR diagnosis accuracy. Initially, 14 CNN models were tested, identifying DenseNet169, MobileNet, and ResNet152 as the most effective. DenseNet169 achieved 87.73% accuracy, 87.75% precision, 87.73% recall, 87.67% F1-score, and 0.8359 Cohen's Kappa. MobileNet followed with 86.40% accuracy, 86.60% precision, 86.40% recall, 86.31% F1-score, and 0.8180 Cohen's Kappa. ResNet152 ranked third with 85.87% accuracy, 86.01% precision, 85.87% recall, 85.83% F1-score, and 0.8188 Cohen's Kappa. In the second stage, deep features from these models were fused and classified using Machine Learning (ML) algorithms (SVM, RF, XGBoost). SVM (sigmoid kernel) performed best with 92.00% accuracy, 91.93% precision, 92.00% recall, 91.91% F1-score, and 0.8930 Cohen's Kappa. The third stage applied meta-heuristic optimization (GA, ABC, PSO, HHO) for feature selection. HHO yielded 94.66% accuracy, precision, and recall, 94.64% F1-score, and 0.9286 Cohen's Kappa. The proposed approach surpassed single CNN models and previous studies in HR diagnosis accuracy and generalization.
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