Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images

ABSTRACT

In early 2020, WHO declared COVID-19, a pandemic disease, which severely infected human inhabitant and health. Researchers, doctors, etc., are finding ways to combat the disease. RT-PCR testing is the initial type of testing that was used to detect whether a patient is COVID (+) or COVID (−).This test kit is costly and the result takes around 6hours. So testing a heavy chunk of the population with RT-PCR is a difficult task. To counter this, X-rays/CT scan-based testing can be used to detect COVID (+) cases to control its spread. X-rays are preferable to CT as they are cheaper and even produce low radiations. The second issue that was noticed during this pandemic period was the availability of doctors. To resolve this issue, a robust automated system for early prediction is essential. Automated systems using machine learning (ML), deep learning (DL) approaches are giving promising results in the detection of COVID (+) cases. In this chapter, we propose a framework for automatic recognition of COVID (+), normal, and pneumonia cases (i.e., multiclassification) over X-ray images. In the proposed method, a dataset of COVID (+), normal, and pneumonia images is used. Initially, the dataset is preprocessed, followed by feature extraction using gray level cooccurrence matrix (GLCM), gray level difference method (GLDM), wavelet transform (WT), and fast Fourier transform (FFT) methods. Features extracted are concatenated to construct a feature pool and these features are used for multiclassification using ML algorithms support vector machines (SVM) and XG Boost. XG Boost performs better than SVM. © 2022 Elsevier Inc. All rights reserved.