Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features
Published: 31 December 2020
by Elsevier BV
Journal of King Saud University - Computer and Information Sciences , Volume 34, pp 3226-3235; https://doi.org/10.1016/j.jksuci.2020.12.010
Abstract: Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%.
Keywords: Covid-19 / Convolutional neural network (CNN) / Discrete wavelet transform (DWT) / Minimum redundancy maximum relevance (mRMR) / Recursive feature elimination (RFE) / Random forest classifier
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