An adaptive feature extraction method for classification of Covid-19 X-ray images

Abstract

This study aims to detect Covid-19 disease in the fastest and most accurate way from X-ray images by developing a new feature extraction method and deep learning model. Partitioned Tridiagonal Enhanced Multivariance Products Representation (PTMEMPR) method is proposed as a new feature extraction method by using matrix partition in TMEMPR method which is known as matrix decomposition method in the literature. The proposed method which provides 99.9% data reduction is used as a preprocessing method in the scheme of the Covid-19 diagnosis. To evaluate the performance of the proposed method, it is compared with the state-of-the-art feature extraction methods which are Singular Value Decomposition(SVD), Discrete Wavelet Transform(DWT) and Discrete Cosine Transform(DCT). Also new deep learning models which are called FSMCov, FSMCov-N and FSMCov-L are developed in this study. The experimental results indicate that the combination of newly proposed feature extraction method and deep learning models yield an overall accuracy 99.8%.