Abstract:
The global emergence of mpox has prompted efforts to strengthen guidelines for
diagnosis, treatment, and prevention to help healthcare providers differentiate
it from other diseases with similar clinical presentations. This study develops
a deep learning-based system to classify mpox and distinguish it from similar
skin lesions (chickenpox, cowpox, and measles) using convolutional neural networks.
Four state-of-the-art architectures, ResNet50, MobileNetV3Large, EfficientNetV2L,
and ConvNeXtBase, were fine-tuned via transfer learning and evaluated
using stratified five-fold cross-validation. To enhance generalization and
mitigate skin color bias, two augmentation strategies were applied: standard transformations
and a color-based method adjusting HSV channels. ConvNeXtBase
achieved the highest performance score across all metrics and was deployed as
both a web application (with Grad-CAM and LIME interpretability) and an offline,
lightweight mobile version (TensorFlow Lite). Clinical validation by a dermatologist
on 40 images showed 100% concordance with expert diagnoses. Explainable
AI revealed that model decisions aligned with clinically relevant lesion
features, improving transparency. This system offers a rapid, cost-effective alternative
to PCR testing, particularly valuable in resource-limited settings and limits
exposure.
