WE-BA: Covid-19 detection by Wavelet Entropy and Bat Algorithm

Abstract

Covid-19 is a kind of fast-spreading pneumonia and has dramatically impacted human life and the economy. As early diagnosis is the most effective method to treat patients and block virus transmission, an accurate, automatic, and effective diagnosis method is needed. Our research proposes a machine learning model (WE-BA) using wavelet entropy for feature extraction to reduce the excessive features, one-layer FNNs for classification, 10-fold cross-validation (CV) to reuse the data for the relatively small dataset, and bat algorithm (BA) as a training algorithm. The experiment eventually achieved excellent performance with an average sensitivity of 75.27% ± 3.25%, an average specificity of 75.88% ± 1.89%, an average precision of 75.75% ± 1.06%, an average accuracy of 75.57% ± 1.21%, an average F1 score of 75.47% ± 1.64%, an average Matthews correlation coefficient of 51.20% ± 2.42%, and an average Fowlkes–Mallows index of 75.49% ± 1.64%. The experiments showed that the proposed WE-BA method yielded superior performance to the state-of-the-art methods. The results also proved the potential of the proposed method for the CT image classification task of Covid-19 on a small dataset.