A deep learning model for CXR-based COVID-19 detection

Laouarem, Ayoub; Kara-Mohamed, Chafia; Bourenane, El-Bay; Hamdi-Cherif, Aboubekeur

doi:10.1109/iceet53442.2021.9659492

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Many studies on the diagnosis of COVID-19 disease through imaging had two limitations. First, some studies were only able to classify the two states of COVID-19 and normal [8] , [12] , [13] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] . Second, some studies have used C.T.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Another problem occurs when a lung disease, such as pneumonia, affects the lungs similarly to COVID-19. Many studies have not distinguished COVID-19 positive and positive pneumonia images [8] , [12] , [13] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] . When multi-class classification is performed, the severity of the problems related to the small dataset volume and the presence of noise is more apparent.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy fine-tuned model for COVID-19 diagnosis

Esmi¹,

Golshan²,

Asadi³

et al. 2023

Computers in Biology and Medicine

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fuzzy fine-tuned model for COVID-19 diagnosis

Esmi¹,

Golshan²,

Asadi³

et al. 2023

Computers in Biology and Medicine

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Autoencoders and Ensemble-Based Solution for COVID-19 Diagnosis from Cough Sound

Hamdi

Moussaoui

Oussalah

et al. 2022

Modelling and Implementation of Complex Systems

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Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19

Majeed

Zhang²,

Hwang³

2022

BDCC

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Federated learning (FL) is one of the leading paradigms of modern times with higher privacy guarantees than any other digital solution. Since its inception in 2016, FL has been rigorously investigated from multiple perspectives. Some of these perspectives are extensions of FL’s applications in different sectors, communication overheads, statistical heterogeneity problems, client dropout issues, the legitimacy of FL system results, privacy preservation, etc. Recently, FL is being increasingly used in the medical domain for multiple purposes, and many successful applications exist that are serving mankind in various ways. In this work, we describe the novel applications and challenges of the FL paradigm with special emphasis on the COVID-19 pandemic. We describe the synergies of FL with other emerging technologies to accomplish multiple services to fight the COVID-19 pandemic. We analyze the recent open-source development of FL which can help in designing scalable and reliable FL models. Lastly, we suggest valuable recommendations to enhance the technical persuasiveness of the FL paradigm. To the best of the authors’ knowledge, this is the first work that highlights the efficacy of FL in the era of COVID-19. The analysis enclosed in this article can pave the way for understanding the technical efficacy of FL in medical field, specifically COVID-19.

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A deep learning model for CXR-based COVID-19 detection

Cited by 5 publications

References 21 publications

A fuzzy fine-tuned model for COVID-19 diagnosis

A fuzzy fine-tuned model for COVID-19 diagnosis

Autoencoders and Ensemble-Based Solution for COVID-19 Diagnosis from Cough Sound

Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19

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