An Evaluation of Hybrid Deep Learning for Monkeypox Detection

The worldwide monkeypox outbreak has presented serious public health issues, requiring the creation of novel diagnostic instruments for early identification. The recent global outbreak of monkeypox underscores the pressing need for the development of new diagnostic tools aimed at early detection. Image-based diagnostic systems may pave the way for implementing deep learning, an increasingly powerful bulwark of artificial intelligence research, to meet the challenges of the near future in defeating the disease. Highlighted in the paper are major strategies such as feature fusion frameworks like MOX-NET, transfer learning using MobileNetV2, and the impact of dataset size on accuracy. Through comparative studies, this paper exposes trade-offs between dataset diversity, computational complexity, and accuracy. Another important limitation indicated by the paper is the overfitting problem and the lack of cross-ethnic generalizability and dependence on small datasets. Increased dataset diversity, along with multi-platform mobile frameworks deployment and research on reliable feature extraction, is a near-future approach.