A Neutrosophic Set Approach on Chest X-rays for Automatic Lung Infection Detection

Abstract

COVID cases and its variants is noted enormously in the past three years. In many medical cases, lung infections such as viral pneumonia, bacterial pneumonia have been initially interpreted as COVID-19. Hence, the proposed work is concentrating on differentiating these lung infection types. This work focuses on using neutrosophic approach of classifying into True (T), False (F) and Indeterminacy (I) set membership to reduce the fuzziness and retain more significant information for feature extraction of the opacity to differentiate the types of lung infections. Initially, the images are preprocessed by alpha-mean and beta-enhancement operation to reduce the indeterminacy and enhancing the image components as the range of lung opacity levels to determine the types. Then, these neutrosophic set enhanced images are fed to various deep learning models like ResNet-50, VGG-16 and XGBoost for classification. Experiments are conducted on ActualMed COVID-19 Chest X-ray and COVID-19 radiography dataset and a comparative analysis on several domain set of images such as the original image, neutrosophic domain (T, I, F) and enhanced neutrosophic domain (alpha, beta) are trained and tested through transfer learning by tuning the various validation parameters. On experimental analysis, an enhanced neutrosophic image achieves a better accuracy of 97.33% among the other domain sets.