DIAGNOSIS OF MONKEYPOX USING INTERPRETABLE DEEP LEARNING

Advanced diagnostic techniques are required due to the recurrence of monkeypox and its potential to create major outbreaks. This study investigates the use of deep learning (DL) to the creation of an interpretable system for diagnosing monkeypox. We provide a thorough analysis of the available research, describe the diagnostic systems that are already in use, and suggest a brand-new deep learning-based approach that places an emphasis on accuracy and interpretability. Convolutional neural networks (CNNs) are used in our implementation to evaluate medical pictures, improving diagnostic accuracy and offering pictorial representations of the model’s predictions. The findings of the experiments show how well the system diagnoses monkeypox and how it might support medical professionals by facilitating clear decision-making.