Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Advancements in the Monkeypox Disease Detection Using Cutting-Edge Strategies: A Literature Survey

Authors
G. Priyanka Princella1, *, A. Sree Lakshmi1
1Department of CSE, Geethanjali College of Engineering and Technology, Hyderabad, Telangana, India
*Corresponding author. Email: princellasalomi03@gmail.com
Corresponding Author
G. Priyanka Princella
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_7How to use a DOI?
Keywords
Monkeypox; YOLOv8; Deep Learning; Object Detection; Medical Imaging
Abstract

Monkeypox, a re-emerging zoonotic disease, has raised global concerns due to its increasing transmission. Accurate and early detection is crucial for effective outbreak management and disease control.Various AI-driven diagnostic techniques have been developed, focusing on deep learning-based image classification and object detection models. Several studies highlight the effectiveness of convolutional neural networks (CNNs), transfer learning, and YOLO-based architectures in identifying monkeypox lesions with high precision. Additionally, biosensor-based detection methods are gaining attention as cost-effective and rapid alternatives to PCR testing. The integration of AI with mobile and web-based diagnostic platforms has further improved accessibility in resource-limited settings. Despite these advancements, challenges such as dataset limitations, model generalizability, and clinical validation remain. Addressing these gaps through enhanced dataset diversity, multimodal diagnostic approaches, and real-time deployment strategies can significantly improve the reliability and scalability of AI-powered Monkeypox detection systems.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_7How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - G. Priyanka Princella
AU  - A. Sree Lakshmi
PY  - 2025
DA  - 2025/11/04
TI  - Advancements in the Monkeypox Disease Detection Using Cutting-Edge Strategies: A Literature Survey
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 65
EP  - 74
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_7
DO  - 10.2991/978-94-6463-858-5_7
ID  - Princella2025
ER  -