CT Image Enhancement Using Variational Mode Decomposition for AI-Enabled COVID Classification

ABSTRACT

SARS-COV-2, also known as COVID-19 pandemic, has escalated calamity in the entire world. Due to its contagious properties, the disease spreads swiftly from person to person via direct contact. More than 210 million people got infected worldwide with more than 18 million active patients as of August 29, 2021. In numerous places, the test process for COVID-19 detection takes longer than 2 days. Once the patient is affected by COVID-19, the obstruction in lungs causes difficulty in analyzing the presence of other lung diseases, such as variants of pneumonia. In this paper, we propose an enhancement technique via the acclaimed signal processing method called variational mode decomposition (VMD) aiding any X-ray image classification method for the detection of pneumonia using convolutional neural networks (CNN). The experiments were conducted on VGG-16 model loaded with ImageNet weights followed by numerous configurations of dense layers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.