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A novel IoT-based deep neural network for COVID-19 detection using a soft-attention mechanism

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Abstract

Learning models from COVID-19 data are conducive to understand this disease. However, the scarcity of labeled data presents certain challenges. Previous works have exploited existing deep neural network models that are pre-trained on large datasets like the ImageNet dataset. However, the generalization of the pre-trained models remains a challenge. The objective of this study is to develop an accurate and reliable model that improves diagnostic accuracy and reduces the chances of misdiagnosis. This, in turn, enables appropriate and timely medical interventions for COVID-19 patients. In this paper, a novel framework is proposed to monitor and predict COVID-19 cases that relies on (1) a layered software architecture and (2) a deep neural network model for data processing. The proposed deep neural network model is based on a pre-trained RegNet model. However, the RegNet has limitations in effectively capturing complex shapes. The receptive field may not handle enough shape. To address this issue, we construct a new block using commonly used convolutional and max-pooling layers. It also incorporates the attention mechanism. This mechanism allows us to control a large receptive field with limited computational resources, highlight relevant features and enhance the discriminative power of the model. Comparative experiments using four different benchmark datasets have shown promising results. The proposed model exhibits high efficiency in accurately distinguishing COVID-19 images, with accuracy ranging from 96.43% to 98.96%. It is advisable that future works explore our proposed framework for more detection problems.

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Data Availability

The ECG signals are obtained from the ECG Images dataset of Cardiac and COVID-19 Patients. The dataset is public and available online at the following link: http://dx.doi.org/10.17632/gwbz3fsgp8.1 The original publication is referenced by [27]. CT scan images are obtained from the SARS-CoV-2 CT-scan dataset and the large COVID-19 CT scan slice dataset. The datasets are public and available online at the following links: SARS-CoV-2 CT-scan dataset: https://www.kaggle.com/datasets/plameneduardo/sarscov2-ctscan-dataset The original publication is referenced by [33] Large COVID-19 CT scan slice dataset: https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset The original publication is referenced by [34]

Abbreviations

CBAM:

Convolutional Block Attention Module

CNN:

Convolutional Neural Network

CT:

Computed Tomography

CXR:

Chest X-Ray

DNN:

Deep Neural Network

ECG:

ElectroCardioGram

IoT:

Internet of Things

NLP:

Natural Language Processing

REGATT:

REGnet with ATTention mechanism

RegNet:

Regular Networks

\(\rho \) :

Pearson correlation

cov:

Covariance

\(\sigma _x\) :

Standard deviation of X

\(\sigma _y\) :

Standard deviation of Y

\(F_{C}\) :

Channel feature map

\(F_{S}\) :

Spatial feature map

E :

Estimation error

\(Z^{*}\) :

Bootstrap estimation

\(\bigotimes \) :

Element-wise multiplication

\(\bigoplus \) :

Element-wise concatenation

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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful comments and suggestions. Additionally, the authors extend their thanks to Associate Professor Hela Ltifi, who served as the scientific advisor. This work was supported by the Ministry of Higher Education and Scientific Research of Tunisia under grant agreement number LR11ES48. Professor Amir Hussain acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) - Grants Ref. EP/M026981/1, EP/T021063/1, EP/T024917/1.

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Authors and Affiliations

  1. Research Groups in Intelligent Machines, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia

    Zeineb Fki, Boudour Ammar, Rahma Fourati & Mounir Ben Ayed

  2. Faculty of Law, Economics and Management Sciences of Jendouba (FSJEGJ), Jendouba, Tunisia

    Rahma Fourati

  3. Imaging Department, Hedi Chaker University Hospital, Faculty of Medicine of Sfax, University of Sfax, 3029, Sfax, Tunisia

    Hela Fendri

  4. Edinburgh Napier University, eh11 4bn, Edinburgh, UK

    Amir Hussain

  5. Faculty of Science of Sfax, University of Sfax, 3038, Sfax, Tunisia

    Mounir Ben Ayed

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Contributions

Zeineb Fki: Conception and design of the study, acquisition of data, analysis and interpretation of data, and drafting the article. Boudour Ammar: Conception and design of the study, acquisition of data, and revising the article critically for important intellectual content. Rahma Fourati: Analysis and interpretation of data, and revising the article critically for important intellectual content. Hela Fendri: Acquisition of data, and revising the article critically for important intellectual content. Amir Hussain: Analysis and interpretation of data, and revising the article critically for important intellectual content. Mounir Ben Ayed: Conception and design of the study, and revising the article critically for important intellectual content. All authors read and approved the final manuscript.

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Correspondence to Zeineb Fki.

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Ethics approval to conduct this research was obtained from the Faculty of Medicine of Sfax in coordination with the imaging department of Hedi Chaker University Hospital.

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Fki, Z., Ammar, B., Fourati, R. et al. A novel IoT-based deep neural network for COVID-19 detection using a soft-attention mechanism. Multimed Tools Appl 83, 54989–55009 (2024). https://doi.org/10.1007/s11042-023-17642-6

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