Correlation-based feature selection using bio-inspired algorithms and optimized KELM classifier for glaucoma diagnosis

Abstract

Reduced computational time and cost, reduced skilled professional resources, and diagnostic accuracy have made medical diagnosis using computer aided systems (CAD) increasingly popular and can be comfortably employed in the diagnosis of many acute and chronic diseases in ophthalmology, cardiology, cancer detection, etc. There seems to be a growing necessity for the computational algorithms to be robust enough, to identify the abnormality in each of the cases, aiding in early diagnosis. In this paper, a glaucoma diagnostic approach based on the wrapper method employing bio-inspired algorithms, and a Kernel-Extreme Learning Machine (KELM) classifier is proposed. The bio-inspired algorithms are deployed to select feature sub-sets, generating three feature sub-sets from the pre-processed fundus images by adopting a correlation-based feature selection (CFS) approach. The selected features are utilized to train the salp-swarm optimization based KELM, which finds the optimal parameters of the KELM classifier network. The proposed methodology is evaluated on the public and private retinal fundus datasets containing 7280 images. The experimental outcome revealed that the system is able to attain a maximum overall accuracy of 99.61% with 99.89% sensitivity and 100% specificity. A 5-fold cross validation showed 98.78% accuracy ensuring a bias-free classification. Further, by experimenting on degraded images (Gaussian, salt-pepper noise images) of the original dataset, the model achieved extreme robustness with 99.3% accuracy. The proposed method is compared with other similar methods, which showed the efficiency of our method. The framework proposed can aid in making clinical decisions for various pathologies like lung infection, diabetic retinopathy, etc.