Open AccessAutomatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features
Author(s) -
S. Rajesh Kannan,
J. Sivakumar,
P. Ezhilarasi
Publication year - 2022
Publication title -
journal of x-ray science and technology
Language(s) - English
Resource type - Journals
eISSN - 1095-9114
pISSN - 0895-3996
DOI - 10.3233/xst-211050
Subject(s) - artificial intelligence , deep learning , covid-19 , medical diagnosis , computer science , classifier (uml) , support vector machine , contagious disease , machine learning , firefly protocol , pattern recognition (psychology) , disease , medicine , infectious disease (medical specialty) , pathology , zoology , biology
Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.