LAUSR

This work introduces the Fourier-Bessel series expansion-based decomposition (FBSED) method, which is an implementation of the wavelet packet decomposition approach in the Fourier-Bessel series expansion domain. The proposed method has been used for the diagnosis of pneumonia caused by the 2019 novel coronavirus disease (COVID-19) using chest X-ray image (CXI) and chest computer tomography image (CCTI). The FBSED method is used to decompose CXI and CCTI into sub-band images (SBIs). The SBIs are then used to train various pre-trained convolutional neural network (CNN) models separately using a transfer learning approach. The combination of SBI and CNN is termed as one channel. Deep features from each channel are fused to get a feature vector. Different classifiers are used to classify pneumonia caused by COVID-19 from other viral and bacterial pneumonia and healthy subjects with the extracted feature vector. The different combinations of channels have also been analyzed to make the process computationally efficient. For CXI and CCTI databases, the best performance has been obtained with only one and four channels, respectively. The proposed model was evaluated using 5-fold and 10-fold cross-validation processes. The average accuracy for the CXI database was 100% for both 5-fold and 10-fold cross-validation processes, and for the CCTI database, it is 97.6% for the 5-fold cross-validation process. Therefore, the proposed method may be used by radiologists to rapidly diagnose patients with COVID-19.