Monkeypox, An Emerging Zoonotic Viral Disease, Has Recently Re-emerged As A Global Health Concern Due To Its Rapid Transmission And Clinical Similarity To Other Skin-related Infections. Early And Accurate Diagnosis Is Essential For Effective Containment And Treatment, Yet Traditional Diagnostic Methods Such As Polymerase Chain Reaction (PCR) Testing And Clinical Evaluation Are Time-consuming, Costly, And Require Expert Resources That Are Often Unavailable In Low-resource Settings. With The Advancement Of Artificial Intelligence, Deep Learning (DL) Has Shown Remarkable Potential In The Automated Diagnosis Of Infectious Diseases From Medical Images, Particularly Skin Lesion Analysis. This Survey Provides A Comprehensive Overview Of Recent Research Efforts In Applying Deep Learning Techniques For Monkeypox Detection And Classification. It Discusses Commonly Used Datasets, Image Preprocessing Methods, Deep Neural Network Architectures, Evaluation Metrics, And Comparative Performance Outcomes. Furthermore, The Paper Highlights Existing Challenges, Including Limited Datasets, Class Imbalance, And The Need For Explainable AI In Clinical Settings. Finally, Potential Future Directions Such As Transfer Learning, Federated Learning, And Multimodal Approaches Are Outlined To Enhance Diagnostic Accuracy And Real-world Applicability. This Survey Aims To Serve As A Reference For Researchers And Healthcare Professionals Seeking To Leverage Deep Learning In Improving The Timely And Reliable Diagnosis Of Monkeypox.
.jpg "A SURVEY ON MONKEYPOX DIAGNOSIS USING DEEP LEARNING")