A Survey on Monkeypox Diagnosis Using Deep Learning

"Are you awaiting the dawn of a new day without health threats, like those we saw before COVID-19 became a global issue? Monkeypox symptoms are similar to those of smallpox and include fever, chills, headaches, and, later, distinctive rashes. The challenge is that these signs resemble common skin conditions like warts or chickenpox, making misdiagnosis likely. This complicates containment and delays intervention. Current diagnostic methods rely on clinical suspicion and lab-based tests, primarily requiring PCR, which may not be easily accessible in resource-limited areas. This overlap, along with delayed diagnosis, leads to continued transmission. Smart, cutting-edge technologies are essential to ensure rapid and accurate detection as the disease spreads. This literature review covers how deep learning algorithms are transforming medical imaging and DNA sequence analysis for monkeypox diagnosis. A deep learning system at the edge could offer more efficient, automated real-time diagnosis. Deploying this at the front lines or integrating with telehealth platforms would speed up the screening of potential cases without requiring expert interpretation. The research also explores AI’s ability to differentiate between the monkeypox virus (MPV) and human papillomavirus (HPV), which causes warts. This illustrates approaches and strategies to tackle this emerging health concern and shows how AI can play a key role in global public health campaigns, beyond just early detection, aiming for broader pandemic prevention efforts.