  The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a
significant public health concern and has spread to over 110 countries
globally. The challenge of clinically diagnosing mpox early on is due, in part,
to its similarity to other types of rashes. Computer-aided screening tools have
been proven valuable in cases where Polymerase Chain Reaction (PCR) based
diagnosis is not immediately available. Deep learning methods are powerful in
learning complex data representations, but their efficacy largely depends on
adequate training data. To address this challenge, we present the "Mpox Skin
Lesion Dataset Version 2.0 (MSLD v2.0)" as a follow-up to the previously
released openly accessible dataset, one of the first datasets containing mpox
lesion images. This dataset contains images of patients with mpox and five
other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox,
and healthy). We benchmark the performance of several state-of-the-art deep
learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2,
EfficientNetB3, InceptionV3, and Xception, to classify mpox and other
infectious skin diseases. In order to reduce the impact of racial bias, we
utilize a color space data augmentation method to increase skin color
variability during training. Additionally, by leveraging transfer learning
implemented with pre-trained weights generated from the HAM10000 dataset, an
extensive collection of pigmented skin lesion images, we achieved the best
overall accuracy of $83.59\pm2.11\%$. Finally, the developed models are
incorporated within a prototype web application to analyze uploaded skin images
by a user and determine whether a subject is a suspected mpox patient.
