Enhanced COVID-19 Classification Using SMOTE-KELM: A Comprehensive Review and Proposed Framework

Abstract

In numerous real-world contexts, the prevalence of abnormal instances in comparison to normal instances is markedly low. This phenomenon, characterized by an imbalanced data distribution, results in an increased likelihood of misclassifying the minority class as the majority. The publicly accessible collection of coronavirus disease-2019 (COVID-19) chest X-ray images has become significantly imbalanced due to the ramifications of the pandemic. To address this challenge, the proposed methodology integrates Histogram of Oriented Gradients (HOG) with the Synthetic Minority Over-sampling Technique (SMOTE) and Kernel-based Extreme Learning Machine (KELM). This process is executed in three phases: initially, the images are subjected to preprocessing, followed by the extraction of features using the HOG algorithm. These extracted features facilitate the generation of synthetic minority class samples, thereby achieving a more balanced dataset. The SMOTE-augmented dataset is subsequently employed for training the KELM, which demonstrates superior performance relative to existing state-of-the-art models. A comprehensive experimental analysis was conducted on four datasets comprising chest X-ray images of COVID-19, pneumonia, tuberculosis, and Healthy lungs. The classification accuracy obtained are 96.72%, 96.38%, 97.15%, and 98.66% on Dataset-1, Dataset-2, Dataset-3, and Dataset-4, respectively.