Abstract
While the globe is still getting over the effects of
the COVID-19 pandemic, the Monkeypox virus is posing a new
threat. The Monkeypox virus is spreading, although not being as
deadly or infectious as COVID-19. New cases are being reported
every day in many different nations. To treat illnesses such
as Monkeypox, early diagnosis and accurate identification of
skin lesions are essential. In this study, we have evaluated deep
learning models for categorizing Monkeypox lesions using two
datasets: Monkeypox Skin Image Dataset (MSID) and Monkeypox Skin Lesion Dataset (MSLD), as well as a merged version
of them. Alongside various deep learning models, including
VGG16, ResNet50, EfficientNetB0, Xception, DenseNet121, and
MobileNetV2, an ensemble of high-performing models was examined. The ensemble model consistently outperforms the individual
models. It has achieved an accuracy of 91% on the MSID dataset,
surpassing DenseNet121 and MobileNetV2, which both scored
88.31%. Similarly, on the MSLD dataset, the ensemble reached
81.90%, while MobileNetV2 and DenseNet121 achieved 77.62%
and 73.57%, respectively. For the merged dataset, the ensemble
performed best with an accuracy of 92%, followed by VGG16
at 84% and DenseNet121 at 83%. These findings indicate that
the ensemble learning technique outperforms individual models
across all datasets, recognizing Monkeypox lesions from skin
images more accurately.
Index Terms—Monkeypox Lesion Detection, Deep Neural
Networks, Transfer Learning, Ensemble Models, Pre-Trained
Architectures, Image-Based Diagnosis
