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A Hybrid Automated Intelligent COVID-19 Classification System Based on Neutrosophic Logic and Machine Learning Techniques Using Chest X-Ray Images

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Advances in Data Science and Intelligent Data Communication Technologies for COVID-19

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 378))

Abstract

To facilitate timely treatment and management of COVID-ap patients, efficient and quick identification of COVID-19 patients is of immense importance during the COVID-19 crisis. Technological developments in machine learning (ML) methods, edge computing, computer-aided medical diagnostic been utilized for COVID-19 Classification. This is mainly because of their ability to deal with Big data and their inherent robustness and ability to provide distinct output characteristics attributed to the underlying application. The contrary transcription-polymerase chain reaction is currently the clinical typical for COVID-19 diagnosis. Besides being expensive, it has low sensitivity and requires expert medical personnel. Compared with RT-PCR, chest X-rays are easily accessible with highly available annotated datasets and can be utilized as an ascendant alternative in COVID-19 diagnosis. Using X-rays, ML methods can be employed to identify COVID-19 patients by quantitively examining chest X-rays effectively. Therefore, we introduce an alternative, robust, and intelligent diagnostic tool for automatically detecting COVID-19 utilizing available resources from digital chest X-rays. Our technique is a hybrid framework that is based on the fusion of two techniques, Neutrosophic techniques (NTs) and ML. Classification features are extracted from X-ray images using morphological features (MFs) and principal component analysis (PCA). The ML networks were trained to classify the chest X-rays into two classes: positive (+ve) COVID-19 patients or normal subjects (or −ve). The experimental results are performed based on a sample from a collected comprehensive image dataset from several hospitals worldwide. The classification accuracy, precision, sensitivity, specificity and F1-score for the proposed scheme was 98.46%, 98.19%, 98.18%, 98.67%, and 98.17%. The experimental results also documented the high accuracy of the proposed pipeline compared to other literature techniques.

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Author information

Authors and Affiliations

  1. Engineering Faculty, University of Mansoura, Mansoura, 35516, Egypt

    Ibrahim Yasser

  2. Communications and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura, 35524, Egypt

    Aya A. Abd El-Khalek

  3. Computer Engineering Department, Engineering Faculty, Benha University, Banha, 13518, Egypt

    Abeer Twakol

  4. Electronics and Communications Engineering Department, Engineering Faculty, University of Mansoura, Mansoura, 35516, Egypt

    Mohy-Eldin Abo-Elsoud & Fahmi Khalifa

  5. Mathematics and Computer Science Department, Faculty of Sciences, University of Port Said, Port Said, 42522, Egypt

    Ahmed A. Salama

Authors
  1. Ibrahim Yasser
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  2. Aya A. Abd El-Khalek
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  3. Abeer Twakol
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  4. Mohy-Eldin Abo-Elsoud
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  5. Ahmed A. Salama
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  6. Fahmi Khalifa
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Corresponding author

Correspondence to Ibrahim Yasser .

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Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Computer Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt

    Sally M. Elghamrawy

  3. Faculty of Electrical Engineering and Computer Science, VŠB-TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

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Yasser, I., Abd El-Khalek, A.A., Twakol, A., Abo-Elsoud, ME., Salama, A.A., Khalifa, F. (2022). A Hybrid Automated Intelligent COVID-19 Classification System Based on Neutrosophic Logic and Machine Learning Techniques Using Chest X-Ray Images. In: Hassanien, AE., Elghamrawy, S.M., Zelinka, I. (eds) Advances in Data Science and Intelligent Data Communication Technologies for COVID-19. Studies in Systems, Decision and Control, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-77302-1_7

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