Published on: April 2026
MONKEYPOX DETECTION USING MODIFIED VGG16 & CUSTOM CNN MODEL
N.Jyotsna P.Pooja Yadav B.Ajith Kumar M.Akshith
A.Mahendar
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Abstract
Monkeypox is a contagious viral disease that has recently gained global attention due to its rapid spread and similarity to other skin-related infections. Early and accurate detection of monkeypox is crucial to control outbreaks and provide timely medical treatment. However, traditional diagnostic methods rely heavily on laboratory testing and expert analysis, which can be time-consuming, expensive, and not easily accessible in all regions.
To address these challenges, this paper proposes an automated monkeypox detection system using advanced deep learning techniques. The proposed approach combines a Modified VGG16 model with a Custom Convolutional Neural Network (CNN) to effectively classify skin lesion images into Monkeypox and Normal categories. The system utilizes image preprocessing techniques such as resizing, normalization, and noise removal, along with data augmentation methods like rotation, flipping
How to Cite this Paper
N.Jyotsna, , Yadav, P., Kumar, B. & M.Akshith, (2026). Monkeypox Detection using Modified VGG16 & Custom CNN Model. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.430
N.Jyotsna, , et al.. "Monkeypox Detection using Modified VGG16 & Custom CNN Model." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.430.
N.Jyotsna, ,P.Pooja Yadav,B.Ajith Kumar, and M.Akshith. "Monkeypox Detection using Modified VGG16 & Custom CNN Model." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.430.
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References
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- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
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- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 16 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.
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