Decision and feature level fusion of deep features extracted from public COVID-19 data-sets.

Content Provider	Europe PMC
Author	Ilhan, Hamza Osman Serbes, Gorkem Aydin, Nizamettin
Abstract	The Coronavirus disease (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe conditions in the patients, the development of rapid and accurate diagnostic tools have gained importance. The real-time reverse transcription-polymerize chain reaction (RT-PCR) is used to detect the presence of Coronavirus RNA by using the mucus and saliva mixture samples taken by the nasopharyngeal swab technique. But, RT-PCR suffers from having low-sensitivity especially in the early stage. Therefore, the usage of chest radiography has been increasing in the early diagnosis of COVID-19 due to its fast imaging speed, significantly low cost and low dosage exposure of radiation. In our study, a computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs) and ensemble learning idea, which can be used by radiologists as a supporting tool in COVID-19 detection, has been proposed. Deep feature sets extracted by using seven CNN architectures were concatenated for feature level fusion and fed to multiple classifiers in terms of decision level fusion idea with the aim of discriminating COVID-19, pneumonia and no-finding classes. In the decision level fusion idea, a majority voting scheme was applied to the resultant decisions of classifiers. The obtained accuracy values and confusion matrix based evaluation criteria were presented for three progressively created data-sets. The aspects of the proposed method that are superior to existing COVID-19 detection studies have been discussed and the fusion performance of proposed approach was validated visually by using Class Activation Mapping technique. The experimental results show that the proposed approach has attained high COVID-19 detection performance that was proven by its comparable accuracy and superior precision/recall values with the existing studies.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8556802&blobtype=pdf
ISSN	0924669X
Journal	Applied Intelligence (Dordrecht, Netherlands) [Appl Intell (Dordr)]
Volume Number	52
DOI	10.1007/s10489-021-02945-8
PubMed Central reference number	PMC8556802
Issue Number	8
PubMed reference number	34764623
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2021-10-30
Publisher Place	New York
Access Restriction	Open
Rights License	This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Subject Keyword	COVID-19 Convolutional neural networks Support vector machines Feature level fusion Decision level fusion Ensemble learning Class activation mapping Transfer learning Multistage learning
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence