Classification of the Chest X-ray Images of COVID-19 Patients Through the Mean Structural Similarity Index

The chest X-ray (CXR) images of healthy patients and patients with COVID-19 are clustered into distinct classes using the mean structural similarity index measure (SSIM). SSIM is intrinsically similar to the human visual system (HVS) and has potential for extracting the information for structural changes in the image to perceive the distortions. The proposed approach is based on local statistical parameters like mean, variance etc., to extract structural information through SSIM. This information is subsequently used for CXR image differentiation, akin to the clinician's visual inspection of these images. As a feature extractor, SSIM is found to effectively classify and characterize COVID-19 patients from the healthy ones from analysis of the CXR images. Our approach of classifying CXR images, based on a single comparative parameter with the use of an ensemble tree classifier, leads to an accuracy equivalent to the recently developed methods using a variety of convolutional neural network (CNN) approaches and is computationally faster. We obtained an accuracy of 97.7% for our proposed models. The obtained results are corroborated through the statistically reliable analysis from the receiver operating characteristic (ROC) curve and confusion matrix. The comparative SSIM index enables the effective use of larger data points for the classifier’s robust training due to cross-correlation between healthy subjects and diseased ones, yielding higher classification accuracy. Our proposed method may find clinical application for classifying patients of COVID-19 using CXR images.