A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images.

Autor: Ieracitano C; DICEAM, University Mediterranea of Reggio Calabria, Via Graziella, Feo di Vito, 89124 Reggio Calabria, Italy., Mammone N; DICEAM, University Mediterranea of Reggio Calabria, Via Graziella, Feo di Vito, 89124 Reggio Calabria, Italy., Versaci M; DICEAM, University Mediterranea of Reggio Calabria, Via Graziella, Feo di Vito, 89124 Reggio Calabria, Italy., Varone G; Department of Neuroscience & Imaging, Universitá degli Studi G. d'Annunzio Chieti e Pescara, Pescara, Italy., Ali AR; Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Scotland, UK., Armentano A; Advanced Diagnostic and Therapeutic Technology Department, Grande Ospedale Metropolitano (GOM) Bianchi-Melacrino-Morelli of Reggio Calabria, Italy., Calabrese G; Advanced Diagnostic and Therapeutic Technology Department, Grande Ospedale Metropolitano (GOM) Bianchi-Melacrino-Morelli of Reggio Calabria, Italy., Ferrarelli A; Advanced Diagnostic and Therapeutic Technology Department, Grande Ospedale Metropolitano (GOM) Bianchi-Melacrino-Morelli of Reggio Calabria, Italy., Turano L; Advanced Diagnostic and Therapeutic Technology Department, Grande Ospedale Metropolitano (GOM) Bianchi-Melacrino-Morelli of Reggio Calabria, Italy., Tebala C; Advanced Diagnostic and Therapeutic Technology Department, Grande Ospedale Metropolitano (GOM) Bianchi-Melacrino-Morelli of Reggio Calabria, Italy., Hussain Z; Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Scotland, UK., Sheikh Z; Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Scotland, UK., Sheikh A; Usher Institute, Edinburgh Medical School, University of Edinburgh, Scotland, UK., Sceni G; Advanced Diagnostic and Therapeutic Technology Department, Grande Ospedale Metropolitano (GOM) Bianchi-Melacrino-Morelli of Reggio Calabria, Italy., Hussain A; School of Computing, Edinburgh Napier University, Scotland, UK., Morabito FC; DICEAM, University Mediterranea of Reggio Calabria, Via Graziella, Feo di Vito, 89124 Reggio Calabria, Italy.
Jazyk: angličtina
Zdroj: Neurocomputing [Neurocomputing (Amst)] 2022 Apr 07; Vol. 481, pp. 202-215. Date of Electronic Publication: 2022 Jan 21.
DOI: 10.1016/j.neucom.2022.01.055
Abstrakt: The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-19 and effective triaging. In this paper, we propose a fuzzy logic based deep learning (DL) approach to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid-19. The developed model here, referred to as CovNNet , is used to extract some relevant features from CXR images, combined with fuzzy images generated by a fuzzy edge detection algorithm. Experimental results show that using a combination of CXR and fuzzy features, within a deep learning approach by developing a deep network inputed to a Multilayer Perceptron (MLP), results in a higher classification performance (accuracy rate up to 81%), compared to benchmark deep learning approaches. The approach has been validated through additional datasets which are continously generated due to the spread of the virus and would help triage patients in acute settings. A permutation analysis is carried out, and a simple occlusion methodology for explaining decisions is also proposed. The proposed pipeline can be easily embedded into present clinical decision support systems.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2022 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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