2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 2 , PP: 86-107, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Eslam 1 * , Mohamed G. Abdelfattah 2 , El-Sayed M. El-Kenawy 3 , Hossam El-Din Moustafa 4
Doi: https://doi.org/10.54216/FPA.160206
After the COVID-19 epidemic, public health awareness increased. A skin viral disease known as monkeypox sparked an emergency alert, leading to numerous reports of infections across numerous European countries. Common symptoms of this disease are fever, high temperatures, and water-filled blisters. This paper presents one of the recent algorithms based on a metaheuristic framework. To improve the performance of monkeypox classification, we introduce the GGO algorithm. Firstly, we employ four pre-trained models (AlexNet, GoogleNet, Resnet-50, and VGG-19) to extract the most common features of monkeypox skin image disease (MSID). Then, we reduce the number of extracted features to select the most distinguishing features for the disease. We make it by using GGO in binary form, which has an average fitness of 0.60068 and a best fitness of 0.50248. Lastly, we apply various optimization algorithms, including the (WWPA) waterwheel plant algorithm, the (DTO) Boosted Dipper Throated Optimization, the (PSO) particle swarm optimizer, the (WAO) whale optimization algorithm, the (GWO) gray wolf optimizer, the (FA) firefly algorithm, and the GGO algorithm, all based on the Convolution Neural Network (CNN), to achieve the best performance. Best Performance is indicated in accuracy and sensitivity; it reached 0.9919 and 0.9895 by GGO. A rigorous statistical analysis test was applied to confirm the validity of our findings. We applied Analysis of Variance ANOVA, and Wilcoxon signed tests, and the results indicated that the value of p was less than 0.005, which strongly supports our hypothesis.
Monkeypox , water-filled blisters , pre-trained model , classification , GGO algorithm.
[1] Altun, M., Gürüler, H., Özkaraca, O., Khan, F., Khan, J., & Lee, Y. (2023). Monkeypox detection using CNN with transfer learning. Sensors, 23(4), 1783.https://doi.org/10.3390/s23041783
[2] Alharbi, A. H., Towfek, S. K., Abdelhamid, A. A., Ibrahim, A., Eid, M. M., Khafaga, D. S., ... & Saber, M. (2023). Diagnosis of monkeypox disease using transfer learning and binary advanced dipper throated optimization algorithm. Biomimetics, 8(3), 313. https://doi.org/10.3390/biomimetics8030313
[3] Muhammed Kalo Hamdan, A., & Ekmekci, D. (2024). Prediction of monkeypox infection from clinical symptoms with adaptive artificial bee colony-based artificial neural network. Neural Computing and Applications, 1-16. https://doi.org/10.1007/s00521-024-09782-z
[4] Savaş, S. (2024). Enhancing Disease Classification with Deep Learning: a Two-Stage Optimization Approach for Monkeypox and Similar Skin Lesion Diseases. Journal of Imaging Informatics in Medicine, 1-23. https://doi.org/10.1007/s10278-023-00941-7
[5] Asif, S., Zhao, M., Li, Y., Tang, F., Ur Rehman Khan, S., & Zhu, Y. (2024). AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects. Archives of Computational Methods in Engineering, 1-33. https://doi.org/10.1007/s11831-024-10091-w
[6] Abdelhamid, A. A., El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., ... & Saber, M. (2022). Classification of monkeypox images based on transfer learning and the Al-Biruni Earth Radius Optimization algorithm. Mathematics, 10(19), 3614. https://doi.org/10.3390/math10193614
[7] Jaradat, A. S., Al Mamlook, R. E., Almakayeel, N., Alharbe, N., Almuflih, A. S., Nasayreh, A., ... & Bzizi, H. (2023). Automated monkeypox skin lesion detection using deep learning and transfer learning techniques. International Journal of Environmental Research and Public Health, 20(5), 4422. https://doi.org/10.3390/ijerph20054422
[8] Saleh, A. I., & Hussien, S. A. (2024). Monkeypox diagnosis based on Dynamic Recursive Gray wolf (DRGW) optimization. Biomedical Signal Processing and Control, 87, 105483. https://doi.org/10.1016/j.bspc.2023.105483
[9] Surati, S., Trivedi, H., Shrimali, B., Bhatt, C., & Travieso-González, C. M. (2023). An Enhanced Diagnosis of Monkeypox Disease Using Deep Learning and a Novel Attention Model Senet on Diversified Dataset. Multimodal Technologies and Interaction, 7(8), 75. https://doi.org/10.3390/mti7080075
[10] Lakshmi, M., & Das, R. (2023). Classification of monkeypox images using LIME-enabled investigation of deep convolutional neural network. Diagnostics, 13(9), 1639. https://doi.org/10.3390/diagnostics13091639
[11] Asif, S., Zhao, M., Li, Y., Tang, F., & Zhu, Y. (2024). CGO-Ensemble: Chaos Game Optimization Algorithm-Based Fusion of Deep Neural Networks for Accurate Mpox Detection. Neural Networks, 106183. https://doi.org/10.1016/j.neunet.2024.106183
[12] Ali, S. N., Ahmed, M. T., Paul, J., Jahan, T., Sani, S. M., Noor, N., & Hasan, T. (2022). Monkeypox skin lesion detection using deep learning models: A feasibility study. arXiv preprint arXiv:2207.03342.
[13] Bala, D., Hossain, M. S., Hossain, M. A., Abdullah, M. I., Rahman, M. M., Manavalan, B., ... & Huang, Z. (2023). MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification. Neural Networks, 161, 757-775.
[14] Campana, M. G., Colussi, M., Delmastro, F., Mascetti, S., & Pagani, E. (2024). A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images. Pervasive and Mobile Computing, 98, 101874. https://doi.org/10.1016/j.pmcj.2023.101874
[15] Campana, M. G., Colussi, M., Delmastro, F., Mascetti, S., & Pagani, E. (2023). Mpox Close Skin Images. https://doi.org/10.5281/zenodo.7948350
[16] Ahsan, M. M., Alam, T. E., Haque, M. A., Ali, M. S., Rifat, R. H., Nafi, A. A. N., ... & Islam, M. K. (2024). Enhancing Monkeypox diagnosis and explanation through modified transfer learning, vision transformers, and federated learning. Informatics in Medicine Unlocked, 45, 101449. https://doi.org/10.1016/j.imu.2024.101449
[17] Ahsan, M. M., Uddin, M. R., & Luna, S. A. (2022). Monkeypox image data collection. arXiv preprint arXiv:2206.01774.
[18] Ahsan, M. M., Uddin, M. R., Ali, M. S., Islam, M. K., Farjana, M., Sakib, A. N., ... & Luna, S. A. (2023). Deep transfer learning approaches for Monkeypox disease diagnosis. Expert Systems with Applications, 216, 119483. https://doi.org/10.1016/J. ESWA.2022.119483.
[19] Demir, F. B., Baygin, M., Tuncer, I., Barua, P. D., Dogan, S., Tuncer, T., ... & Acharya, U. R. (2024). MNPDenseNet: Automated Monkeypox Detection Using Multiple Nested Patch Division and Pretrained DenseNet201. Multimedia Tools and Applications, 1-23. https://doi.org/10.1007/s11042-024-18416-4
[20] Bala, D., & Hossain, M. S. (2022). Monkeypox skin images dataset (msid). Mendeley Data, 6, 2023.
[21] Kundu, D., Rahman, M. M., Rahman, A., Das, D., Siddiqi, U. R., Alam, M. G. R., ... & Ali, Z. (2024). Federated Deep Learning for Monkeypox Disease Detection on GAN-Augmented Dataset. IEEE Access. doi={10.1109/ACCESS.2024.3370838}}
[22] M. Ahsan. "MONKEYPOX IMAGE DATA COLLECTION, https://github.com/mahsan2/Monkeypoxdataset-2022." (Accessed 01.09.2022).
[23] Thorat, R., & Gupta, A. (2024). Transfer learning-enabled skin disease classification: the case of monkeypox detection. Multimedia Tools and Applications, 1-19.
https://doi.org/10.1007/s11042-024-18750-7
[24] Yadav, S., & Qidwai, T. (2024). Machine learning-based monkeypox virus image prognosis with feature selection and advanced statistical loss function. Medicine in Microecology, 19, 100098. https://doi.org/10.1016/j.medmic.2024.100098
[25] Kaggle. Monkeypox skin lesion dataset. Available: https://www.kaggle.com/dat asets/nafin59/monkeypox-skin-lesion-dataset; 2022.
[26] Raha, A. D., Gain, M., Debnath, R., Adhikary, A., Qiao, Y., Hassan, M. M., ... & Islam, S. M. S. (2024). Attention to Monkeypox: An Interpretable Monkeypox Detection Technique Using Attention Mechanism. IEEE Access. doi={10.1109/ACCESS.2024.3385099}}
[27] Dermnet. Accessed: Sep. 2023. [Online]. Available: http://www.dermnet.com/
[28] Khafaga, D. S., Ibrahim, A., El-Kenawy, E. S. M., Abdelhamid, A. A., Karim, F. K., Mirjalili, S., ... & Ghoneim, M. E. (2022). An Al-Biruni earth radius optimization-based deep convolutional neural network for classifying monkeypox disease. Diagnostics https://doi.org/10.3390/diagnostics12112892
[29] Monkeypox Skin Images Dataset (MSID). Available online: https://www.kaggle.com/datasets/dipuiucse/monkeypoxskinimagedataset (accessed on 2 October 2022
[30] Bala, D., Hossain, M. S., Hossain, M. A., Abdullah, M. I., Rahman, M. M., Manavalan, B., ... & Huang, Z. (2023). MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification. Neural Networks, 161, 757-775. https://doi.org/10.1016/j.neunet.2023.02.022.
[31] Lakshmi, M., & Das, R. (2023). Classification of monkeypox images using LIME-enabled investigation of deep convolutional neural network. Diagnostics, 13(9), 1639. https://doi.org/10.3390/diagnostics13091639
[32] Ahsan, M. M., Uddin, M. R., Ali, M. S., Islam, M. K., Farjana, M., Sakib, A. N., ... & Luna, S. A. (2023). Deep transfer learning approaches for Monkeypox disease diagnosis. Expert Systems with Applications, 216, 119483. https://doi.org/10.1016/j.eswa.2022.119483
[33] El-kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Abdelhamid, A. A., Eid, M. M., & Ibrahim, A. (2024). Greylag goose optimization: Nature-inspired optimization algorithm. Expert Systems with Applications, 238, 122147. https://doi.org/10.1016/j.eswa.2023.122147
