3066-280XISSN (Online)
Volume 5 , Issue 1 , PP: 83-103, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Mostafa Abotaleb 1 *
Doi: https://doi.org/10.54216/MOR.050105
Monkeypox (mpox) has emerged as a significant re-emerging zoonotic threat, with the 2022–2023 global outbreak underscoring the need for rapid detection, genomic monitoring, and predictive intervention strategies. This work presents a structured synthesis of three major research domains: (1) detection and classification, encompassing convolutional neural networks (CNNs), transformer-based architectures, capsule networks, transfer learning, feature selection, ensemble methods, and explainability tools applied to lesion images for accurate diagnosis; (2) genomics, prediction, and reviews, covering time series modeling of viral genome mutations using long short-term memory (LSTM) networks, phylogenetic analysis, mutation hotspot identification, and critical reviews of AI-based diagnostic methods and metaheuristic optimization strategies; and (3) intervention support, focusing on outbreak forecasting, gradient boosting risk models, and non-stationary LSTM frameworks for scenario planning and resource allocation. Across categories, recurring challenges include limited and imbalanced datasets, inconsistent reporting, and the gap between algorithmic accuracy and clinical or operational integration. This synthesis highlights methodological trends, identifies limitations, and outlines research priorities: developing multicenter datasets, leveraging multimodal integration of phenotype and genotype, adopting federated and semi-supervised learning to address data scarcity, and coupling predictive models with operational feasibility assessments. By linking technical innovation with practical outbreak management needs, this work bridges the gap between computational research and public health application, offering a roadmap for mpox preparedness and control in both endemic and non-endemic regions.
Convolutional Neural Networks , Long Short-Term Memory , Monkeypox , Genomic Analysis , Outbreak Forecasting
[1] Ahmed Muhammed Kalo Hamdan and Dursun Ekmekci. Prediction of monkeypox infection from clinical symptoms with adaptive artificial bee colony-based artificial neural network. Neural Computing and Applications, 36(22):13715–13730, August 2024.
[2] D. Devarajan, P. Dhana Lakshmi, S. Krishnaveni, and S. Senthilkumar. Human monkeypox disease prediction using novel modified restricted Boltzmann machine-based equilibrium optimizer. Scientific Reports, 14(1):17612, 2024. Publisher: Nature Publishing Group UK London.
[3] Mehdhar SAM Al-Gaashani, Reem Alkanhel, Dina SM Hassan, Abduljabbar S. Ba Mahel, Ahmed Aziz, Mashael M. Khayyat, and Ammar Muthanna. Optimizing Monkeypox Lesions Detection with a Lightweight Hybrid Model. IEEE Access, 2025. Publisher: IEEE.
[4] Dilber Çetinta¸s. Efficient monkeypox detection using hybrid lightweight CNN architectures and optimized SVM with grid search on imbalanced data. Signal, Image and Video Processing, 19(4):336, April 2025.
[5] Marwa F. Mohamed and Ahmed Hamed. Monkeypox Optimizer: A Bio-Inspired Evolutionary Optimization Algorithm and its Engineering Applications. Available at SSRN 4632034.
[6] Ahmet Ciran and Erdal Özbay. Optimization-based feature selection in deep learning methods for monkeypox skin lesion detection. In 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pages 1–6. IEEE, 2023.
[7] Saddam Hussain Khan, Rashid Iqbal, and Saeeda Naz. A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection, November 2023. arXiv:2311.10754 [eess].
[8] Hager Saleh, Sherif Mostafa, Alshimaa H. Ismail, Sumit Srivastava, and Saeed Hamood Alsamhi. SwinPSO-SVM: A Novel Hybrid Model for Monkeypox Early Detection. IEEE Access, 2024. Publisher: IEEE.
[9] Vijayalakshmi Chintamaneni, Baranikunta Hari Krishna, Merugu Suresh, Krishnaveni Bukkaptanm, Canavoy Narahari Sujatha, Kuraparthi Swaraja, and Pala Mahesh Kumar. Deep Learning-Based Diagnostic Model for Automated Detection of Monkeypox: Introducing MonkeypoxNet. Traitement du Signal, 41(1), 2024.
[10] Ahmadreza Shateri, Negar Nourani, Morteza Dorrigiv, and Hamid Nasiri. An Explainable NatureInspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm, April 2025. arXiv:2504.17540 [cs].
[11] A. Muthulakshmi, CH Venkata Siva Prasad, G. Balachandran, and S. Ranjith. Optimized Global Aware Siamese Network based Monkeypox disease classification using skin images. Biomedical Signal Processing and Control, 101:107125, 2025. Publisher: Elsevier.
[12] Poonam Sharma, Bhisham Sharma, Dhirendra Prasad Yadav, Surbhi Bhatia Khan, and Ahlam Almusharraf. Leveraging Edge Optimize Vision Transformer for Monkeypox Lesion Diagnosis on Mobile Devices. Computers, Materials & Continua, 83(2), 2025.
[13] Azka Mir, Attique Ur Rehman, Sabeen Javaid, and Tahir Muhammad Ali. An intelligent technique for the effective prediction of monkeypox outbreak. In 2023 3rd International Conference on Artificial Intelligence (ICAI), pages 220–226. IEEE, 2023.
[14] M. Vasudevan, B. Kishore, and Srinivas Kumar. Framework for Early Human Monkeypox. Deep Learning and Blockchain Technology for Smart and Sustainable Cities, page 340, 2025. Publisher: CRC Press.
[15] Roheet Bhatnagar, Esraa Hassan, Abeer Saber, Mahmoud Y. Shams, and Samar Elbedwehy. Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images. In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), pages 1897–1902. IEEE, 2024.
[16] Imran Ahmed and Misbah Ahmad. Interpretable deep learning for monkeypox lesion classification: A study using model-agnostic explainability techniques. In 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pages 0202–0209. IEEE, 2024.
[17] Ahmed I. Saleh and Shaimaa A. Hussien. Disease Diagnosis Based on Improved Gray Wolf Optimization (IGWO) and Ensemble Classification. Annals of Biomedical Engineering, July 2023.
[18] Omneya Attallah. MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning. DIGITAL HEALTH, 9:20552076231180054, January 2023.
[19] Refat Khan Pathan, Mohammad Amaz Uddin, Ananda Mohan Paul, Md Imtiaz Uddin, Zuhal Y. Hamd, Hanan Aljuaid, and Mayeen Uddin Khandaker. Monkeypox genome mutation analysis using a timeseries model based on long short-term memory. PLoS One, 18(8):e0290045, 2023. Publisher: Public Library of Science San Francisco, CA USA.
[20] M. Nuthal Srinivasan, Mohamed Yacin Sikkandar, Maryam Alhashim, and M. Chinnadurai. Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system. Scientific Reports, 15(1):3296, 2025. Publisher: Nature Publishing Group UK London.
[21] Farhana Yasmin, Md Mehedi Hassan, Mahade Hasan, Sadika Zaman, Chetna Kaushal, Walid El-Shafai, and Naglaa F. Soliman. PoxNet22: A fine-tuned model for the classification of monkeypox disease using transfer learning. Ieee Access, 11:24053–24076, 2023. Publisher: IEEE.
[22] Hilal Güven and Ahmet Saygılı. Monkeypox Diagnosis Using MRMR-Based Feature Selection and Hybrid Deep Learning Models: ResNet50V2, NASNetMobile, and InceptionV3. International Scientific and Vocational Studies Journal, 9(1):173–182, 2025. Publisher: Umut SARAY.
[23] Saddam Hussain Khan and Rashid Iqbal. RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis, October 2024. arXiv:2410.01216 [eess].
[24] Arpit Ayushman Mishra, Sahitya Mishra, Aditya A. Shastri, and Ahmed Alkhayyat. A Hyper-tuned Metrics based Augmented CNN Model for Accurate Prediction of Monkeypox Risk. In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC), pages 1–4. IEEE, 2024.
[25] Sohaib Asif, Ming Zhao, Yangfan Li, Fengxiao Tang, Saif Ur Rehman Khan, and Yusen Zhu. AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects. Archives of Computational Methods in Engineering, 31(6):3585–3617, August 2024.
[26] Imran Zafar, Faheem Kanwal, Muhammad Azmat, Ahsanullah Unar, Ijaz Ali, Waqas Yousaf, Qurat Ul Ain, Mohd Ashraf Rather, Nemat Ali, Mohammad Rashid Khan, Amandeep Singh Dhanju, and Rohit Sharma. Comprehensive Genomic, Mutation, Phylogenetic, and Statistical Analysis of the Monkeypox Virus Across Multiple Countries. Indian Journal of Microbiology, 65(2):1321–1344, June 2025.
[27] Rohan Thorat and Aditya Gupta. Transfer learning-enabled skin disease classification: the case of monkeypox detection. Multimedia Tools and Applications, 83(35):82925–82943, March 2024.
[28] Abdullah Al Noman, Hasib Fardin, Gunjan Chhabra, Shayma Sultana, Rezaul Haque, Md Redwan Ahmed, Swarna Hasibunnahar, and Keshav Kaushik. Monkeypox Lesion Classification: A Transfer Learning Approach for Early Diagnosis and Intervention. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), volume 7, pages 247–254. IEEE, 2024.
[29] Emir Oncu. Combining CNNs and symptom data for monkeypox virus detection. International Journal of Complexity in Applied Science and Technology, 1(4):330–349, 2025.
[30] Ibne Hassan, Raida Mobashshira Tahsin, Sadia Shara Toma, and Tausif Tazwar Quadria Utchhash. Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM. PhD Thesis, Brac University, 2023.
[31] O. Onyijen, F. Ikpotokin, Celestine Agwi, F. Sadiq, S. Awojide, and M. Odighi. A Predictive Model for Data-Driven Insights into MonkeyPox Virus Outbreaks and Case Trends in Africa. 2024.
[32] Omnia M. Osama, Khder Alakkari, Mostafa Abotaleb, and El-Sayed M. El-Kenawy. Forecasting global monkeypox infections using lstm: A non-stationary time series analysis. In 2023 3rd international conference on electronic engineering (ICEEM), pages 1–7. IEEE, 2023.
[33] Arnab Mitra Utsab. MonkeyPox skin disease classification. PhD Thesis, Brac University, 2025.
[34] Navdeep Singh Sodhi, Vishnu Kant, Sofia Singla, Upinder Kaur, and Salil Bharany. Analyzing Machine Learning Frameworks for Detection of Monkeypox Virus Across the Globe. In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), pages 852–856. IEEE, 2024.
[35] Imtiaj Ahmed, Rayan, Sayma Akter Tihany, and Adnan Mahmud. Enhancing Monkeypox Disease Detection Using Computer Vision-Based Approaches and Deep Learning. In Amit Kumar Bairwa, Varun Tiwari, Santosh Kumar Vishwakarma, Milan Tuba, and Thittaporn Ganokratanaa, editors, Computation of Artificial Intelligence and Machine Learning, volume 2184, pages 346–356. Springer Nature Switzerland, Cham, 2025. Series Title: Communications in Computer and Information Science.
[36] Madhumita Pal, Ahmed Mahal, Ranjan K. Mohapatra, Ahmad J. Obaidullah, Rudra Narayan Sahoo, Gurudutta Pattnaik, Sovan Pattanaik, Snehasish Mishra, Mohammed Aljeldah, Mohammed Alissa, Mustafa A. Najim, Amer Alshengeti, Bashayer M. AlShehail, Mohammed Garout, Muhammad A. Halwani, Ahmad A. Alshehri, and Ali A. Rabaan. Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification. ACS Omega, 8(35):31747–31757, September 2023.
[37] Someswar Pal and Amit Kumar Mishra. Artificial Intelligence-Based Framework for Predicting Monkeypox Disease. In 2023 International Conference on Computer Science and Emerging Technologies (CSET), pages 1–5. IEEE, 2023.
[38] Someswar Pal, Amit Kumar Mishra, Kanad Ray, and Saurav Mallik. Deep Learning Approaches for Monkeypox Virus Prediction: A Comparative Study. In Mufti Mahmud, M. Shamim Kaiser, Anirban Bandyopadhyay, Kanad Ray, and Shamim Al Mamun, editors, Proceedings of Trends in Electronics and Health Informatics, volume 1034, pages 357–370. Springer Nature Singapore, Singapore, 2025. Series Title: Lecture Notes in Networks and Systems.
[39] Azmath Mubeen and Uma N. Dulhare. Metaheuristic Algorithms for the Classification and Prediction of Skin Lesions: A Comprehensive Review. In Uma N. Dulhare and Essam Halim Houssein, editors, Machine Learning and Metaheuristics: Methods and Analysis, pages 107–137. Springer Nature Singapore, Singapore, 2023. Series Title: Algorithms for Intelligent Systems.
[40] H. S. Madhu, Sreekanth Rallapalli, and Ramya Thatikonda. Three Dimensional DenseUNet with CKHA Segmentation Technique for Monkeypox Disease Prediction. In 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT), pages 1–8. IEEE, 2023.
