Comprehensive Summary
This study aimed to improve AI-based detection of monkeypox and other dermatological abnormalities, using attention-based models such as vision transformers, convolutional neural networks, and deep learning models. Mousa et al. aimed to train deep learning models, primarily variants of EfficientNet, and a Five-fold cross-validation was used to ensure accuracy of diagnosis when compared to previously used models. The Xception model had the highest validation accuracy, of 99.92%, followed by MobileNetV2 and Swin Transformer. Both MobileNetV2 and Swin Transformer were deemed dependable for practical application due to their high training accuracy scores. The two also proved able to accurately identify positive cases and minimize false positives, making them promising options for AI-based monkeypox diagnostic models.
Outcomes and Implications
More rapid detection and diagnosis of monkeypox can allow for containment precautions to be taken effectively. It can also be a practical alternative to PCR testing in areas with limited medical resources. Due to the high statistics of accuracy and precision, as well as low incidence of misclassification the researchers suggest integration of this diagnostic tool into a clinical setting immediately. This could be especially useful in telemedicine, where physical examinations requiring patient samples may not be possible. While physicians should not opt to use this tool over traditional diagnostic techniques altogether, it can serve as a supplementary diagnostic tool.