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Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm

Author

Listed:
  • Abdelaziz A. Abdelhamid

    (Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • El-Sayed M. El-Kenawy

    (Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt)

  • Nima Khodadadi

    (Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33199, USA)

  • Seyedali Mirjalili

    (Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea)

  • Doaa Sami Khafaga

    (Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Amal H. Alharbi

    (Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Abdelhameed Ibrahim

    (Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

  • Marwa M. Eid

    (Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt)

  • Mohamed Saber

    (Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa City 11152, Egypt)

Abstract

The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%.

Suggested Citation

  • Abdelaziz A. Abdelhamid & El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Doaa Sami Khafaga & Amal H. Alharbi & Abdelhameed Ibrahim & Marwa M. Eid & Mohamed Saber, 2022. "Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-29, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3614-:d:932337
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    References listed on IDEAS

    as
    1. El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
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    Cited by:

    1. Marwa M. Eid & El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Ehsaneh Khodadadi & Mostafa Abotaleb & Amal H. Alharbi & Abdelaziz A. Abdelhamid & Abdelhameed Ibrahim & Ghada M. Amer & Am, 2022. "Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    2. Ameera S. Jaradat & Rabia Emhamed Al Mamlook & Naif Almakayeel & Nawaf Alharbe & Ali Saeed Almuflih & Ahmad Nasayreh & Hasan Gharaibeh & Mohammad Gharaibeh & Ali Gharaibeh & Hanin Bzizi, 2023. "Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques," IJERPH, MDPI, vol. 20(5), pages 1-20, March.
    3. El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Tatiana Makarovskikh & Mostafa Abotaleb & Faten Khalid Karim & Hend K. Alkahtani & Abdelaziz A. Abdelhamid & Marwa M. Eid & Takahiko Horiu, 2022. "Metaheuristic Optimization for Improving Weed Detection in Wheat Images Captured by Drones," Mathematics, MDPI, vol. 10(23), pages 1-30, November.

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