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

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 s...

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Vydáno v:	Biomimetics (Basel, Switzerland) Ročník 8; číslo 3; s. 313
Hlavní autoři:	Alharbi, Amal H., Towfek, S. K., Abdelhamid, Abdelaziz A., Ibrahim, Abdelhameed, Eid, Marwa M., Khafaga, Doaa Sami, Khodadadi, Nima, Abualigah, Laith, Saber, Mohamed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 01.07.2023 MDPI
Témata:	Accuracy Algorithms biological mechanism Classification COVID-19 Datasets Deep learning Diagnosis dipper throated optimization Epidemics feature selection Human monkeypox Lyme disease Medical imaging Medical research monkeypox detection Mpox Neural networks Pandemics Skin diseases Skin lesions Smallpox Transfer learning Vaccines Viral infections United States deep learning monkeypox detection transfer learning biological mechanism dipper throated optimization feature selection
ISSN:	2313-7673, 2313-7673
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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.
AbstractList	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. 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, -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, -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. 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.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.
Audience	Academic
Author	Alharbi, Amal H. Ibrahim, Abdelhameed Eid, Marwa M. Abdelhamid, Abdelaziz A. Khafaga, Doaa Sami Abualigah, Laith Khodadadi, Nima Saber, Mohamed Towfek, S. K.
AuthorAffiliation	16 Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt 13 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 8 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA 11 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan 5 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt 15 School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia 7 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt 10 Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon 1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman
AuthorAffiliation_xml	– name: 14 School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia – name: 12 MEU Research Unit, Middle East University, Amman 11831, Jordan – name: 4 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia – name: 5 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt – name: 10 Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon – name: 1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia – name: 3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt – name: 7 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt – name: 6 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt – name: 13 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan – name: 15 School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia – name: 2 Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA – name: 11 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan – name: 16 Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Mansoura P.O. Box 11152, Egypt – name: 9 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan – name: 8 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
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SubjectTerms	Accuracy Algorithms biological mechanism Classification COVID-19 Datasets Deep learning Diagnosis dipper throated optimization Epidemics feature selection Human monkeypox Lyme disease Medical imaging Medical research monkeypox detection Mpox Neural networks Pandemics Skin diseases Skin lesions Smallpox Transfer learning Vaccines Viral infections
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Title	Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm
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