Abstract

A rare and potentially fatal viral disease, monkeypox predominantly impacts non-human primates, including humans. It is critical to establish an early and precise diagnosis of monkeypox in order to contain outbreaks and administer opportune treatment effectively. Deep learning and convolutional neural networks (CNNs) have demonstrated considerable potential in the domain of medical image diagnosis in recent times. The current research paper introduces an innovative methodology for categorizing images of monkeypox utilizing the cutting-edge deep learning framework, EfficientNetB3. Outstanding performance has been exhibited by EfficientNetB3, a CNN architecture renowned for its high efficiency and accuracy across a range of image classification tasks. This study presents the adaptation and fine-tuning of EfficientNetB3 to classify monkeypox. Data augmentation techniques are implemented to improve the model's capacity to extrapolate to diverse variations present in images of monkeypox. A substantial collection of images about monkeypox, comprising laboratory samples and clinical photographs, is utilized by the proposed model. In terms of accurately and precisely classifying images of monkeypox, experimental results demonstrate the efficacy of our method. Several metrics are employed to assess the efficacy of the mode. The performance of the trained EfficientNetB3 model in differentiating monkeypox from various skin conditions and infections is encouraging, as this capability is critical for the timely and precise diagnosis of the disease.