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Federated learning based Covid-19 detection.
Chowdhury, Deepraj; Banerjee, Soham; Sannigrahi, Madhushree; Chakraborty, Arka; Das, Anik; Dey, Ajoy; Dwivedi, Ashutosh Dhar.
  • Chowdhury D; Department of Electronics and Communication International Institute of Information Technology Naya Raipur Naya Raipur Chhattisgarh India.
  • Banerjee S; Department of Electronics and Communication International Institute of Information Technology Naya Raipur Naya Raipur Chhattisgarh India.
  • Sannigrahi M; Department of Artificial Intelligence Amity University Kolkata West Bengal India.
  • Chakraborty A; Department of Computer Science RCCIIT Kolkata West Bengal India.
  • Das A; Department of Computer Science RCCIIT Kolkata West Bengal India.
  • Dey A; Department of Electronics and Telecommunication Jadavpur University Kolkata West Bengal India.
  • Dwivedi AD; Department of Digitalization, Centre for Business Data Analytics Copenhagen Business School Frederiksberg Denmark.
Expert Syst ; : e13173, 2022 Nov 02.
Article in English | MEDLINE | ID: covidwho-2313706
ABSTRACT
The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study Language: English Journal: Expert Syst Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study Language: English Journal: Expert Syst Year: 2022 Document Type: Article