Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox

Abstract

Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To address the challenge, we proposed "Super Monitoring", a real-time visualization technique employing artificial intelligence (AI) and Internet technology to diagnose earlier-stage mpox cheaply, conveniently, and quickly. Concretely, AI-mediated "Super Monitoring" (mpox-AISM) integrates deep learning models, data augmentation, self-supervised learning, and cloud services. According to publicly accessible datasets, mpox-AISM's Precision, Recall, Specificity, and F1-score in diagnosing mpox reach 99.3%, 94.1%, 99.9%, and 96.6%, respectively, and it achieves 94.51% accuracy in diagnosing mpox, six like-mpox skin disorders, and normal skin. With the Internet and communication terminal, mpox-AISM has the potential to perform real-time and accurate diagnosis for earlier-stage mpox in real-world scenarios, thereby preventing mpox outbreak.

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A Simple Framework for Contrastive Learning of Visual Representations
• SurfaceNet: Adversarial SVBRDF Estimation from a Single Image
• Monkeypox virus detection using pre-trained deep learning-based approaches
• Self-supervised learning methods and applications in medical imaging analysis: A survey
• Image Data Augmentation for Deep Learning: A Survey
• Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study
• Does deep learning model calibration improve performance in class-imbalanced medical image classification?