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002_July 2023 Artificial_Intelligence_and_Blockchain_enabled_smart_healthcare_system_for_monitoring_and_detection_of_COVID_19_in_biomedical_images.pdf (1.16 MB)

Artificial Intelligence and Blockchain enabled smart healthcare system for monitoring and detection of COVID-19 in biomedical images

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journal contribution
posted on 2024-03-19, 14:58 authored by Imran Ahmed, Abdellah Chehri, Gwanggil Jeon

Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilised in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualisation approach to all of the tests. As a result, the architecture achieves a classification accuracy of 96%, thus producing excellent results.



  • Yes

Publication title

IEEE/ACM Transactions on Computational Biology and Bioinformatics




Association for Computing Machinery (ACM)

File version

  • Accepted version


  • eng

Affiliated with

  • School of Computing and Information Science Outputs


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