  Monkeypox virus (MPXV) is a zoonotic virus that poses a significant threat to
public health, particularly in remote parts of Central and West Africa. Early
detection of monkeypox lesions is crucial for effective treatment. However, due
to its similarity with other skin diseases, monkeypox lesion detection is a
challenging task. To detect monkeypox, many researchers used various
deep-learning models such as MobileNetv2, VGG16, ResNet50, InceptionV3,
DenseNet121, EfficientNetB3, MobileNetV2, and Xception. However, these models
often require high storage space due to their large size. This study aims to
improve the existing challenges by introducing a CNN model named MpoxSLDNet
(Monkeypox Skin Lesion Detector Network) to facilitate early detection and
categorization of Monkeypox lesions and Non-Monkeypox lesions in digital
images. Our model represents a significant advancement in the field of
monkeypox lesion detection by offering superior performance metrics, including
precision, recall, F1-score, accuracy, and AUC, compared to traditional
pre-trained models such as VGG16, ResNet50, and DenseNet121. The key novelty of
our approach lies in MpoxSLDNet's ability to achieve high detection accuracy
while requiring significantly less storage space than existing models. By
addressing the challenge of high storage requirements, MpoxSLDNet presents a
practical solution for early detection and categorization of monkeypox lesions
in resource-constrained healthcare settings. In this study, we have used
"Monkeypox Skin Lesion Dataset" comprising 1428 skin images of monkeypox
lesions and 1764 skin images of Non-Monkeypox lesions. Dataset's limitations
could potentially impact the model's ability to generalize to unseen cases.
However, the MpoxSLDNet model achieved a validation accuracy of 94.56%,
compared to 86.25%, 84.38%, and 67.19% for VGG16, DenseNet121, and ResNet50,
respectively.
