Automatic classification of glaucoma from fundus images is a vital diagnostic tool for Computer-Aided Diagnosis System (CAD). In this work, a novel fused feature extraction technique and ensemble classifier fusion is proposed for diagnosis of glaucoma. The proposed method comprises of three stages. Initially, the fundus images are subjected to preprocessing followed by feature extraction and feature fusion by Intra-Class and Extra-Class Discriminative Correlation Analysis (IEDCA). The feature fusion approach eliminates between-class correlation while retaining sufficient Feature Dimension (FD) for Correlation Analysis (CA). The fused features are then fed to the classifiers namely Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) for classification individually. Finally, Classifier fusion is also designed which combines the decision of the ensemble of classifiers based on Consensus-based Combining Method (CCM). CCM based Classifier fusion adjusts the weights iteratively after comparing the outputs of all the classifiers. The proposed fusion classifier provides a better improvement in accuracy and convergence when compared to the individual algorithms. A classification accuracy of 99.2% is accomplished by the two-level hybrid fusion approach. The method is evaluated on the public datasets High Resolution Fundus (HRF) and DRIVE datasets with cross dataset validation.

从眼底图像自动分类青光眼是计算机辅助诊断系统 (CAD) 的重要诊断工具。在这项工作中，提出了一种新的融合特征提取技术和集成分类器融合用于青光眼的诊断。所提出的方法包括三个阶段。最初，眼底图像经过预处理，然后通过类内和类外判别相关分析 (IEDCA) 进行特征提取和特征融合。特征融合方法消除了类间相关性，同时为相关性分析 (CA) 保留了足够的特征维度 (FD)。然后将融合的特征馈送到分类器，即支持向量机 (SVM)、随机森林 (RF) 和 K-最近邻 (KNN) 进行单独分类。最后，还设计了分类器融合，它结合了基于共识的组合方法（CCM）的分类器集合的决策。基于 CCM 的分类器融合在比较所有分类器的输出后迭代调整权重。与单个算法相比，所提出的融合分类器在准确性和收敛性方面提供了更好的改进。99.2% 的分类准确率是通过两级混合融合方法实现的。该方法在公共数据集高分辨率眼底 (HRF) 和具有交叉数据集验证的 DRIVE 数据集上进行评估。与单个算法相比，所提出的融合分类器在准确性和收敛性方面提供了更好的改进。99.2% 的分类准确率是通过两级混合融合方法实现的。该方法在公共数据集高分辨率眼底 (HRF) 和具有交叉数据集验证的 DRIVE 数据集上进行评估。与单个算法相比，所提出的融合分类器在准确性和收敛性方面提供了更好的改进。99.2% 的分类准确率是通过两级混合融合方法实现的。该方法在公共数据集高分辨率眼底 (HRF) 和具有交叉数据集验证的 DRIVE 数据集上进行评估。