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

Ieracitano, Cosimo; Mammone, Nadia; Versaci, Mario; Varone, Giuseppe; Ali, Abder-Rahman; Armentano, Antonio; Calabrese, Grazia; Ferrarelli, Anna; Turano, Lorena; Tebala, Carmela; Hussain, Zain; Sheikh, Zakariya; Sheikh, Aziz; Sceni, Giuseppe; Hussain, Amir; Morabito, Francesco Carlo


Cosimo Ieracitano

Nadia Mammone

Mario Versaci

Giuseppe Varone

Abder-Rahman Ali

Antonio Armentano

Grazia Calabrese

Anna Ferrarelli

Lorena Turano

Carmela Tebala

Zain Hussain

Zakariya Sheikh

Aziz Sheikh

Giuseppe Sceni

Francesco Carlo Morabito


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.


Ieracitano, C., Mammone, N., Versaci, M., Varone, G., Ali, A., Armentano, A., Calabrese, G., Ferrarelli, A., Turano, L., Tebala, C., Hussain, Z., Sheikh, Z., Sheikh, A., Sceni, G., Hussain, A., & Morabito, F. C. (2022). A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing, 481, 202-215.

Journal Article Type Article
Acceptance Date Jan 14, 2022
Online Publication Date Jan 21, 2022
Publication Date 2022-04
Deposit Date Dec 2, 2022
Journal Neurocomputing
Print ISSN 0925-2312
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 481
Pages 202-215
Keywords Chest X-ray, Convolutional Neural Network, Covid-19, explainable Artificial Intelligence, Fuzzy logic, Portable systems
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