International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Powered Monkeypox Detection Using Deep Learning |
|---|---|
| ABSTRACT | Monkeypox is a viral disease that has become a global public health concern. The disease spreads through close physical contact and is marked by fever, swollen lymph nodes, and visible skin lesions. Early detection is crucial for preventing transmission and ensuring timely treatment. Traditional lab-based diagnosis may not be easily available in remote areas. An automated detection system using machine learning can aid in early screening. In this paper, we propose an image-based monkeypox detection system that uses convolutional neural networks (CNN). The model analyzes images of skin lesions and classifies them as infected or non-infected. Experimental results show that our method achieves good accuracy and can help in preliminary diagnosis. This approach may boost awareness and early responses during outbreaks. |
| AUTHOR | SAIRABI MUJWAR, BHUMIKA KHARADE, SWARA KULKARNI Lecturer, Department of Computer Technology, Bharati Vidyapeeth’s Jawaharlal Nehru Institute of Technology (Poly), Pune, Maharashtra, India. Student, Department of Computer Technology, Bharati Vidyapeeth’s Jawaharlal Nehru Institute of Technology (Poly), Pune, Maharashtra, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403079 |
| pdf/79_AI Powered Monkeypox Detection Using Deep Learning.pdf | |
| KEYWORDS | |
| References | [1] World Health Organization, “Monkeypox Fact Sheet,” WHO, 2023. [2] Centers for Disease Control and Prevention (CDC), “2022–2023 Monkeypox Outbreak Global Map,” CDC, 2023. [3] A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, pp. 115–118, 2017. [4] O. Stephen et al., “Deep Learning for Medical Image Analysis: A Comprehensive Review,” IEEE Reviews in Biomedical Engineering, 2019. [5] Recent Advances in Monkeypox Detection Using Deep Learning Techniques, Journal of Medical Systems, 2023. |
