Automatic diagnosis of COVID-19 and pneumonia using FBD method

Abstract

Novel coronavirus (COVID-19) is spreading rapidly and has taken millions of lives worldwide. A medical study has shown that COVID-19 affects the lungs of patients and shows the symptoms of pneumonia. X-ray images with artificial intelligence (AI) can be useful for a fast and accurate diagnosis of COVID19. It can also solve the problem of less testing kits and fewer doctors. In this paper, we have introduced the Fourier-Bessel series expansion-based dyadic decomposition (FBD) method for image decomposition. This FBD is used to decompose an X-ray image into subband images. Obtained subband images are then fed to ResNet50 pre-trained convolution neural network (CNN) individually. Deep features from each CNN are ensembled using operations, namely; maxima (max), minima (min), average (avg), and fusion (fus). Ensemble CNN features are then fed to the softmax classifier. In the study, a total of 750 X-ray images are collected. Out of 750 X-ray images, 250 images are of pneumonia patients, 250 of COVID-19 patients, and 250 healthy subjects. The proposed model has provided an overall accuracy of 98.6% using fus ensemble ResNet-50 CNN model.