Glaucoma classification based on intra-class and extra-class discriminative correlation and consensus ensemble classifier.

Glaucoma classification based on intra-class and extra-class discriminative correlation and consensus ensemble classifier.
Genomics. 2020 May 26;:
Authors: Kishore B, Ananthamoorthy NP
Abstract
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-lev...

Source: Genomics - May 25, 2020 Category: Genetics & Stem Cells Authors: Kishore B, Ananthamoorthy NP Tags: Genomics Source Type: research