Interpretable AI Models for Effective Monkeypox Diagnosis

The need of early discovery and response in preventing future pandemics is shown by the worldwide spread of monkeypox illness after the COVID-19 pandemic. This research used Transfer Learning (TL) approaches to improve the performance of four separate DL models, namely VGG16, ResNet50, MobileNetV2, and VGG19, and found that Deep Learning (DL) provides a cost-effective and accessible option for illness prediction. Top performances with exceptional accuracy were modified versions of MobileNetV2 and VGG19 models.These findings highlight the importance of DL-based early illness identification as a vital weapon in the battle against new health dangers and have important consequences for disease management and preventive initiatives, particularly in underserved areas.The integration of the Hybrid MobileNetV2 model, which attained an impressive 100% accuracy, is a major step forward in the study and suggests a very dependable diagnostic tool for monkeypox. An easy way to test with real users is to add a Flask front-end interface to the project. To further guarantee that the Monkeypox diagnostic service is accessible and used by authorized persons only, user authentication has been included.