Łukasiewicz Logic and Artificial Neural Networks

Nola, Antonio Di; Vitale, Gaetano

doi:10.1007/978-3-030-31041-7_8

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Some DL-based support schemes use customized networks, while others use pretrained transfer learning-based networks. Along with some allied fields such as the Internet of Things (IoT), computer vision, big data, and smartphones, AI can contribute to managing COVID-19 [14][15][16][17][23][24][25][26]. A number of ML algorithms, such as support vector machine (SVM), linear regression, logistic regression, and a number of DL algorithms, including convolutional neural networks (CNNs), are studied in the literature [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Federated Learning for Screening COVID-19: A Review

Mondal,

Bharati,

Podder

et al. 2023

BioMedInformatics

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Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated.

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