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An Intelligent Model for Rapid Diagnosis of Patients with COVID-19 Based on ANFIS

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021 (AISI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 100))

Abstract

Early classification of coronavirus disease (COVID-19) has a vital role in controlling the rapid spread of this disease and saving patients’ lives. The fast spread of COVID-19 increased the diagnostic encumbrance of radiologists. Computed tomography (CT) imaging is an effective tool to detect COVID-19 but requires a radiology expert and takes a large time. The machine learning (ML) based models are considered as one of the important ways to analyze the CT images to detect the COVID-19 cases. Therefore, this paper has been focused on finding a suitable machine learning algorithm that can automatically analyze CT images to extract and detect COVID-19 pneumonia features with higher accuracy. This research used classifiers as the Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision tree (DT) to classify CT images into COVID-19 and NonCOVID-19. This paper also designed an Adaptive Neuro-Fuzzy Inference System (ANFIS) based model to achieve a fast and accurate diagnosis of Covid-19. The CT exams of other lung diseases were included in the dataset to improve the model performance. So, the NonCOVID-19 results of the proposed models include the other lung diseases. Confusion matrices and ROC analyses of the proposed models are analyzed using 5-fold cross-validation. During the study and testing of several proposed models, the experimental results showed that the performance of the ANFIS proposed model achieved the best performance with an accuracy of 98.63% and 0.02 s testing time. In the main purpose of this study is to shed light on the ML-based COVID-19 detection models for researchers working with ML techniques and help avoid proven failures, especially for small imprecise datasets.

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Author information

Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering (Shoubra), Benha University, Cairo, Egypt

    Aya Hossam

  2. Nanotechnology Central Lab, Electronics Research Institute (ERI), Cairo, Egypt

    Ahmed Fawzy

  3. Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, Egypt

    Basem E. Elnaghi & Ahmed Magdy

Authors
  1. Aya Hossam
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  2. Ahmed Fawzy
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  3. Basem E. Elnaghi
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  4. Ahmed Magdy
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Corresponding author

Correspondence to Ahmed Fawzy .

Editor information

Editors and Affiliations

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

    Aboul Ella Hassanien

  2. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. Fujian University of Technology, Shulin District, New Taipei, Taiwan

    Kuo-Chi Chang

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. University of Salford, Manchester, UK

    Tarek Gaber

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Hossam, A., Fawzy, A., Elnaghi, B.E., Magdy, A. (2022). An Intelligent Model for Rapid Diagnosis of Patients with COVID-19 Based on ANFIS. In: Hassanien, A.E., Snášel, V., Chang, KC., Darwish, A., Gaber, T. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021. AISI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-89701-7_30

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