Comparative Analysis of Deep CNN, Transfer Learning, and Proposed Ensemble Architecture for Monkeypox Detection

DOI

10.2991/978-94-6239-664-7_4How to use a DOI?

Keywords

Monkey Pox; Skin disease; Skin lesion; Comparative Analysis

Abstract

Monkeypox detection and prevention depend on proper and timely diagnosis of the disease. Manual clinical examination is risky for healthcare staff, time-consuming, and costly. Hence, computer-aided diagnosis of monkeypox is highly valuable. In this work, multiple deep learning algorithms are employed to distinguish monkeypox using skin lesion images. The Monkeypox Skin Images Dataset (MSID) contains six classes: Chickenpox, Cowpox, Measles, Hand-Foot-Mouth Disease (HFMD), Monkeypox, and healthy skin. To classify monkeypox, six independent CNNs, six transfer learning models, and a ranked-based ensemble model were used. Among the CNNs, DenseNet201 achieved the highest accuracy of 99.68%, while SeResNet152 obtained the lowest accuracy of 96.67%. For transfer learning models, DenseNet201 again achieved the best performance with 94.53% accuracy, whereas VGG19 yielded the lowest at 63.35%. Finally, the ranked-based ensemble model (DVX), employing DenseNet201, VGG19, and Xception, achieved 100% accuracy, outperforming all individual CNNs and transfer learning approaches. These findings indicate that ensemble deep learning is a highly promising method for automated monkeypox detection on skin images, with strong potential for clinical applications and early disease diagnosis.

Copyright

© 2026 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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TY  - CONF
AU  - Md Rofiqul Bari
AU  - Jannatul Ferdaus
AU  - Farjana Akter Tonny
AU  - Tanzina Bithi
AU  - Talha Zubaer
AU  - Amir Sohel
PY  - 2026
DA  - 2026/06/08
TI  - Comparative Analysis of Deep CNN, Transfer Learning, and Proposed Ensemble Architecture for Monkeypox Detection
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 34
EP  - 49
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_4
DO  - 10.2991/978-94-6239-664-7_4
ID  - Bari2026
ER  -