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

INTRODUCTION: Covid-19 is a kind of fast-spreading pneumonia and has dramatically impacted human life and the economy. OBJECTIVES: As early diagnosis is the most effective method to treat patients and block virus transmission, an accurate, automatic, and effective diagnosis method is needed. METHODS: 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. RESULTS: 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%. CONCLUSION: 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.