Next-generation healthcare: Digital twin technology and Monkeypox Skin Lesion Detector network enhancing monkeypox detection - Comparison with pre-trained models

Abstract

The rise of digital healthcare has led to the adoption of various technologies aimed at enhancing health operations, patient well-being, and healthcare costs. Digital Twin (DT) technology is a pivotal innovation in this domain. Monkeypox virus (MPXV), a zoonotic virus, poses a significant public health risk, particularly in remote regions of Central and West Africa. Early diagnosis of monkeypox lesions is crucial but challenging due to similarities with other skin conditions. Many studies have employed deep-learning models to detect the monkeypox virus. However, those models often require substantial storage space. This research introduces the Monkeypox Skin Lesion Detector Network (MxSLDNet), an automated digital twin framework designed to enhance digital healthcare operations by enabling early detection and classification of monkeypox and non-monkeypox lesions. Monkeypox Skin Lesion Detector Network (MxSLDNet) significantly advances monkeypox lesion identification, outperforming conventional models like Visual Geometry Group 19 (VGG-19), Densely Connected Network 121 (DenseNet-121), Efficient Network B4 (EfficientNet-B4) and Residual Network 101 (ResNet-101) regarding precision, recall, F1-score, and accuracy, while requiring less storage. This innovation addresses the critical issue of storage demands, making the Monkeypox Skin Lesion Detector Network (MxSLDNet) a viable solution for early monkeypox lesion detection in resource-limited healthcare settings. Utilizing the “Monkeypox Skin Lesion Dataset” with 1428 monkeypox and 1764 non-monkeypox images, Monkeypox Skin Lesion Detector Network (MxSLDNet) achieves high recall, precision, and F1-scores of 0.96, 0.95, and 0.95, respectively. Integrating digital twins into healthcare promises to create a scalable, intelligent, and comprehensive health ecosystem, enhancing treatments by connecting patients and healthcare providers.