Transfer Learning with EfficientNetB3 and ResNet50V2 For MPox Detection

Introduction: Monkeypox (MPox) is a continuing global public health concern for zoonotic disease. Accurate and prompt diagnoses of MPox are key to programmatic disease control. Established key diagnostic modalities are resource intensive, requiring technology and facilities, clinical examination and laboratory testing are thus time-consuming. Conversely, machine learning and deep learning techniques provide fast and automatic diagnostic solutions. Objectives:To diagnose monkeypox from clinical imagery this work proposes a transfer learning-based method utilizing the EfficientNetB3 and ResNet50V2 models. These models go very well with image classification and prediction. Although this approach is intended to be useful for therapeutic usage in the detection of monkeypox at low resources and inadquate healthcare facilities. This research demonstrates how transfer learning can be utilized to implement pre-trained models for Monkeypox detection with high accuracy thereby reducing the necessity of fully labeled datasets. Methods: Using transfer learning, this study explores two pre-trained efficient model architectures – EfficientNetB3 and ResNet50V2, to classify skin lesion images exhibited in MPox. Pre-trained on large scale datasets, these models are fine-tuned using the Monkeypox Skin Lesion Dataset (MSLD) v2. 0 to improve prediction accuracy. Simple rotating, scaling, scaling, brightness adjustment, and other methods are used for data augmentation to enrich the dataset and promote generalization. Convolutional neural network is a deep learning architecture which, especially transfer learning based network, significantly improves detection and robustness of MPox with high accuracy and less qualification of the data set labels. Results: The results of this study demonstrate that deep learning—more especially, transfer learning—can be an effective instrument for managing and detecting outbreaks of monkeypox early on, perhaps leading to better patient outcomes and less strain on healthcare systems. It is essential to combine cutting-edge transfer learning techniques with well-established deep learning algorithms to increase prediction accuracy and clarify the complexities of the ongoing worldwide monkeypox outbreak. Our work presents novel approaches to address this significant health issue while highlighting the ongoing significance of ground-breaking methods. Our proposed models with accuracy of 0.89 and 0.99 have outperformed the existing models in MPox detection. Conclusions: The suggested transfer learning architecture outperforms the most recent models in terms of MPox detection capabilities. Better early MPox identification, more effective treatment planning, and ultimately better patient care could be the outcomes of these developments.