Whale-Optimized Kernel Extreme Learning Machine for Chest X-ray Image Classification

Chest X-ray images are critical in detecting diseases such as Covid-19, pneumonia, and lung opacity. This paper presents an approach to enhance the accuracy of chest X-ray image classification by implementing the Whale Optimization Algorithm (WOA) to the Kernel Extreme Learning Machine (KELM). The initial step involves applying the Singular Value Decomposition (SVD) method on chest X-ray images for efficient feature extraction. Following this, Principal Component Analysis (PCA) and Lasso regularization techniques are applied for dimensionality reduction and feature selection respectively, which significantly lowers the likelihood of overfitting. This paper significantly improves the classification accuracy by optimizing the regularization parameter in KELM and the width parameter in the Radial Basis Function using WOA. Experimental results underscore the superiority of the WOA-KELM, recording an accuracy rate of 94.9008% in a three-class (Covid-19, pneumonia, normal) X-ray image classification task, a drastic improvement over the conventional ELM (88%) and unoptimized KELM (68.7819%).