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

Hamza Osman Ilhan*, Gorkem Serbes, Nizamettin Aydin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)8551-8571
Number of pages21
JournalApplied Intelligence
Volume52
Issue number8
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • COVID-19
  • Class activation mapping
  • Convolutional neural networks
  • Decision level fusion
  • Ensemble learning
  • Feature level fusion
  • Multistage learning
  • Support vector machines
  • Transfer learning

Fingerprint

Dive into the research topics of 'Decision and feature level fusion of deep features extracted from public COVID-19 data-sets'. Together they form a unique fingerprint.

Cite this