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A Comparative Analysis of CNN-Based Pretrained Models for the Detection and Prediction of Monkeypox

Authors:
Rejwan Bin Sulaiman, Sourav Saha, Trina Chakraborty, Tithi Paul

Addresses:
Department of Computer Science and Technology, Northumbria University, Middlesex Street, London, United Kingdom.  Department of Computer Science and Engineering, Shahjalal University of Science and Technology,  Kumargaon, Sylhet, Bangladesh. Department of Computer Science, University of Barishal, Kornokathi, Barishal, Bangladesh. 

Abstract:

Following the COVID-19 epidemic, Monkeypox, which is an uncommon illness, elicited concerns from specialists in the medical field. It is a cause for concern because Monkeypox is difficult to diagnose in its early stages, as its symptoms may resemble those of measles and chickenpox. In addition, there is a knowledge gap among healthcare experts regarding this rare ailment. As a consequence, there is a pressing need to develop a new method to combat and predict the disease at the initial stages of viral infection. This research included a variety of pretrained convolutional neural network (CNN) models. The models that were used included VGG-16, VGG-19, Resnet50, Inception-V3, DenseNet, Xception, MobileNetV2, Alexnet, LeNet, and majority Voting. To conduct this study, several different datasets were integrated, including the following: Monkeypox against chickenpox, Monkeypox versus measles, Monkeypox versus normal, and Monkeypox versus all disorders. In the case of Monkeypox versus chickenpox, majority voting scored 97%, while Xception scored 79% for Monkeypox versus measles, MobileNetV2 scored 96% for Monkeypox versus normal, and LeNet scored 80% for Monkeypox versus all.

Keywords: Pretrained Models; Healthcare Professionals; Restnet50 and Inception-V3; DenseNet and Xception; MobileNetV2 Architecture; Alexnet and LeNet; Majority Voting; Monkeypox Disease.

Received on: 02/11/2024, Revised on: 11/01/2025, Accepted on: 03/03/2025, Published on: 07/09/2025

DOI: 10.69888/FTSHSL.2025.000499

FMDB Transactions on Sustainable Health Science Letters, 2025 Vol. 3 No. 3, Pages: 125-134

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