Exploring best-performing radiomic features with combined multilevel discrete wavelet decompositions for multiclass COVID-19 classification using chest X-ray images

Abstract

Discrete wavelet transforms have been applied in many machine learning models for the analysis of COVID-19; however, little is known about the impact of combined multilevel wavelet decompositions for the disease identification. This study proposes a computer-aided diagnosis system for addressing the combined multilevel effects of multiscale radiomic features on multiclass COVID-19 classification using chest X-ray images. A two-level discrete wavelet transform was applied to an optimal region of interest to obtain multiscale decompositions. Both approximation and detail coefficients were extensively investigated in varying frequency bands through 1240 experimental models. High dimensionality in the feature space was managed using a proposed filter- and wrapper-based feature selection approach. A comprehensive comparison was conducted between the bands and features to explore best-performing ensemble algorithm models. The results indicated that incorporating multilevel decompositions could lead to improved model performance. An inclusive region of interest, encompassing both lungs and the mediastinal regions, was identified to enhance feature representation. The light gradient-boosting machine, applied on combined bands with the features of basic, gray-level, Gabor, histogram of oriented gradients and local binary patterns, achieved the highest weighted precision, sensitivity, specificity, and accuracy of 97.50 %, 97.50 %, 98.75 %, and 97.50 %, respectively. The COVID-19-versus-the-rest receiver operating characteristic area under the curve was 0.9979. These results underscore the potential of combining decomposition levels with the original signals and employing an inclusive region of interest for effective COVID-19 detection, while the feature selection and training processes remain efficient within a practical computational time.