An efficient hybrid classification approach for COVID-19 based on Harris Hawks Optimization and Salp Swarm Optimization

TL;DR

This paper introduces a hybrid feature selection method combining Harris Hawks Optimization and Salp Swarm Optimization to improve COVID-19 classification accuracy using chest X-ray images.

Contribution

It presents a novel hybrid binary optimization algorithm (HHOSSA) that enhances feature selection for COVID-19 detection from X-ray images.

Findings

HHOSSA outperforms WOA and GWO in feature selection.

Achieved up to 98% accuracy with XGBoost and KNN classifiers.

Demonstrated effectiveness on 800 chest X-ray images.

Abstract

Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO's performance using the Salp algorithm's power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN.