ABSTRACT
To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly. © 2023 IEEE.