ABSTRACT
The rapid global spread of COVID-19 poses a huge threat to human security. Accurate and rapid diagnosis is essential to contain COVID-19, and an artificial intelligence-based classification model is an ideal solution to this problem. In this paper, we propose a method based on wavelet entropy and Cat Swarm Optimization to classify chest CT images for the diagnosis of COVID-19 and achieve the best performance among similar methods. The mean and standard deviation of sensitivity is 74.93 ± 2.12, specificity is 77.57 ± 2.25, precision is 76.99 ± 1.79, accuracy is 76.25 ± 1.49, F1-score is 75.93 ± 1.53, Matthews correlation coefficient is 52.54 ± 2.97, Feature Mutual Information is 75.94 ± 1.53. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
The rapid global spread of COVID-19 disease poses a huge threat to human health and the global economy. The rapid increase in the number of patients diagnosed has strained already scarce healthcare resources to track and treat Covid-19 patients in a timely and effective manner. The search for a fast and accurate way to diagnose Covid-19 has attracted the attention of many researchers. In our study, a deep learning framework for the Covid-19 diagnosis task was constructed using wavelet entropy as a feature extraction method and a feedforward neural network classifier, which was trained using an adaptive particle swarm algorithm. The model achieved an average sensitivity of 85.14% ± 2.74%, specificity of 86.76% ± 1.75%, precision of 86.57% ± 1.36%, accuracy of 85.95% ± 1.14%, and F1 score of 85.82% ± 1.30%, Matthews correlation coefficient of 71.95 ± 2.26%, and Fowlkes-Mallows Index of 85.83% ± 1.30%. Our experiments validate the usability of wavelet entropy-based feature extraction methods in the medical image domain and show the non-negligible impact of different optimisation algorithms on the models by comparing them with other models. © 2022, Springer Nature Switzerland AG.