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Interpretation of Immunofluorescence Slides by Deep Learning Techniques: Anti-nuclear Antibodies Case Study

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Abstract

Nowadays, diseases are increasing in numbers and severity by the hour. Immunity diseases, affecting 8% of the world population in 2017 according to the World Health Organization (WHO), is a field in medicine worth attention due to the high rate of disease occurrence classified under this category. This work presents an up-to-date review of state-of-the-art immune diseases healthcare solutions. We focus on tackling the issue with modern solutions such as Deep Learning to detect anomalies in the early stages hence providing health practitioners with efficient tools. We rely on advanced deep learning techniques such as Convolutional Neural Networks (CNN) to fulfill our objective of providing an efficient tool while providing a proficient analysis of this solution. The proposed solution was tested and evaluated by the immunology department in the Principal Military Hospital of Instruction of Tunis, which considered it a very helpful tool.

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Acknowledgment

The authors would like to thank Prince Sultan University for financially supporting the conference attendance fees.

Author information

Authors and Affiliations

  1. Military Academy of Fondouk Jedid, 8012, Nabeul, Tunisia

    Oumar Khlelfa, Aymen Yahyaoui & Anwer Ncibi

  2. Science and Technology for Defense Lab (STD), Ministry of National Defense, Tunis, Tunisia

    Aymen Yahyaoui

  3. Immunology Department, The Principal Military Hospital of Instruction of Tunis, Tunis, Tunisia

    Mouna Ben Azaiz & Ezzedine Gazouani

  4. Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia

    Adel Ammar & Wadii Boulila

  5. RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia

    Wadii Boulila

Authors
  1. Oumar Khlelfa

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  2. Aymen Yahyaoui

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  3. Mouna Ben Azaiz

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  4. Anwer Ncibi

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  5. Ezzedine Gazouani

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  6. Adel Ammar

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  7. Wadii Boulila

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Corresponding authors

Correspondence toOumar Khlelfa,Aymen Yahyaoui orWadii Boulila.

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Nunez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Khlelfa, O.et al. (2023). Interpretation of Immunofluorescence Slides by Deep Learning Techniques: Anti-nuclear Antibodies Case Study. In: Nguyen, N.T.,et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_9

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JPY 3498
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eBook
JPY 13727
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