Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features

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%. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

This paper proposes an intelligent approach to detect Covid-19 from chest X-ray images by hybridizing deep CNN and DWT features. Experimental results demonstrate that the approach outperforms existing methods with an overall accuracy of over 98.5%.