Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm

Amal H Alharbi; S K Towfek; Abdelaziz A Abdelhamid; Abdelhameed Ibrahim; Marwa M Eid; Doaa Sami Khafaga; Nima Khodadadi; Laith Abualigah; Mohamed Saber

doi:10.3390/biomimetics8030313

Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm

Biomimetics (Basel). 2023 Jul 16;8(3):313. doi: 10.3390/biomimetics8030313.

Authors

Amal H Alharbi¹, S K Towfek^{2

3}, Abdelaziz A Abdelhamid^{4

5}, Abdelhameed Ibrahim⁶, Marwa M Eid⁷, Doaa Sami Khafaga¹, Nima Khodadadi⁸, Laith Abualigah^{9

10

11

12

13

14

15}, Mohamed Saber¹⁶

Affiliations

¹ Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
² Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA.
³ Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt.
⁴ Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia.
⁵ Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt.
⁶ Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt.
⁷ Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt.
⁸ Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA.
⁹ Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan.
¹⁰ Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.
¹¹ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan.
¹² MEU Research Unit, Middle East University, Amman 11831, Jordan.
¹³ Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan.
¹⁴ School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia.
¹⁵ School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia.
¹⁶ Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt.

Abstract

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.

Keywords: biological mechanism; deep learning; dipper throated optimization; feature selection; monkeypox detection; transfer learning.

Grants and funding

PNURSP2023R 120/Princess Nourah bint Abdulrahman University