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

Rafid Mostafiz; Mohammad Shorif Uddin; Nur-A- Alam; Md Mahfuz Reza; Mohammad Motiur Rahman

doi:10.1016/j.jksuci.2020.12.010

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

J King Saud Univ Comput Inf Sci. 2022 Jun;34(6):3226-3235. doi: 10.1016/j.jksuci.2020.12.010. Epub 2020 Dec 31.

Authors

Rafid Mostafiz^{1

2}, Mohammad Shorif Uddin³, Nur-A- Alam^{1

2}, Md Mahfuz Reza¹, Mohammad Motiur Rahman¹

Affiliations

¹ Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh.
² Department of Computer Science and Engineering, Dhaka International University, Bangladesh.
³ Department of Computer Science and Engineering, Jahangirnagar University, Bangladesh.

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: Convolutional neural network (CNN); Covid-19; Discrete wavelet transform (DWT); Minimum redundancy maximum relevance (mRMR); Random forest classifier; Recursive feature elimination (RFE).