\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{SAM: Self-augmentation mechanism for COVID-19 detection using chest
X-ray Images}



\author[1]{Usman Muhammad }%
\author[2,2,2]{Md Ziaul Hoque }%
\author[1]{Mourad Oussalah }%
\author[1]{Anja Keskinarkaus }%
\author[1]{Tapio Seppänen }%
\author[1]{Pinaki Sarder }%
\affil[1]{Affiliation not available}%
\affil[2]{University of Oulu}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
COVID-19 is a rapidly spreading viral disease and has affected over 100
countries worldwide. The numbers of casualties and cases of infection
have escalated particularly in countries with weakened healthcare
systems. Recently, reverse transcription-polymerase chain reaction
(RT-PCR) is the test of choice for diagnosing COVID-19. However, current
evidence suggests that COVID-19 infected patients are mostly stimulated
from a lung infection after coming in contact with this virus.
Therefore, chest X-ray (i.e., radiography) and chest CT can be a
surrogate in some countries where PCR is not readily available. This has
forced the scientific community to detect COVID-19 infection from X-ray
images and recently proposed machine learning methods offer great
promise for fast and accurate detection. Deep learning with
convolutional neural networks (CNNs) has been successfully applied to
radiological imaging for improving the accuracy of diagnosis. However,
the performance remains limited due to the lack of representative X-ray
images available in public benchmark datasets. To alleviate this issue,
we propose a self-augmentation mechanism for data augmentation in the
feature space rather than in the data space using reconstruction
independent component analysis (RICA). Specifically, a unified
architecture is proposed which contains a deep convolutional neural
network (CNN), a feature augmentation mechanism, and a bidirectional
LSTM (BiLSTM). The CNN provides the high-level features extracted at the
pooling layer where the augmentation mechanism chooses the most relevant
features and generates low-dimensional augmented features. Finally,
BiLSTM is used to classify the processed sequential information. We
conducted experiments on three publicly available databases to show that
the proposed approach achieves the state-of-the-art results with
accuracy of 97\%, 84\% and 98\%. Explainability analysis has been
carried out using feature visualization through PCA projection and t-SNE
plots.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

