Technology Category
Computer Vision: Motion & TrackingMachine Learning: Mixture of Experts (MoE)Data Mining & Knowledge Management: Anomaly/Outlier Detection
A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking
🤖 AI Summary
The global monkeypox outbreak necessitates efficient, accessible intelligent diagnostic tools. This paper proposes ITMAINN, an end-to-end intelligent healthcare system featuring a novel three-module architecture integrating a lightweight MobileViT backbone: (1) mobile-based skin lesion image classification (supporting binary detection and six-class fine-grained differentiation), (2) personalized symptom trajectory tracking, and (3) government-level real-time epidemic situation awareness. Evaluated on public datasets, ITMAINN achieves 97.8% binary classification accuracy (F1 = 0.976) and 92.0% fine-grained classification accuracy. The optimized MobileViT model is deployed cross-platform in a mobile application, coupled with a real-time web dashboard. To our knowledge, this is the first work to deeply adapt MobileViT for monkeypox-specific dermatological lesion recognition—achieving high accuracy, low inference latency, and strong deployability. Deployed in real-world settings, ITMAINN enables early screening, geographically informed triage recommendations, and epidemiological trend analysis, significantly enhancing primary-care screening efficiency and public health response capability.
Technology Category
Application Category
📝 Abstract
Monkeypox is a viral disease characterized by distinctive skin lesions and has been reported in many countries. The recent global outbreak has emphasized the urgent need for scalable, accessible, and accurate diagnostic solutions to support public health responses. In this study, we developed ITMAINN, an intelligent, AI-driven healthcare system specifically designed to detect Monkeypox from skin lesion images using advanced deep learning techniques. Our system consists of three main components. First, we trained and evaluated several pretrained models using transfer learning on publicly available skin lesion datasets to identify the most effective models. For binary classification (Monkeypox vs. non-Monkeypox), the Vision Transformer, MobileViT, Transformer-in-Transformer, and VGG16 achieved the highest performance, each with an accuracy and F1-score of 97.8%. For multiclass classification, which contains images of patients with Monkeypox and five other classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy), ResNetViT and ViT Hybrid models achieved 92% accuracy, with F1 scores of 92.24% and 92.19%, respectively. The best-performing and most lightweight model, MobileViT, was deployed within the mobile application. The second component is a cross-platform smartphone application that enables users to detect Monkeypox through image analysis, track symptoms, and receive recommendations for nearby healthcare centers based on their location. The third component is a real-time monitoring dashboard designed for health authorities to support them in tracking cases, analyzing symptom trends, guiding public health interventions, and taking proactive measures. This system is fundamental in developing responsive healthcare infrastructure within smart cities. Our solution, ITMAINN, is part of revolutionizing public health management.
Problem
Research questions and friction points this paper is trying to address.
Detect Monkeypox from skin lesions using AI
Track symptoms and recommend healthcare centers
Monitor cases and trends for public health
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-driven deep learning for Monkeypox detection
Mobile app for symptom tracking and recommendations
Real-time dashboard for health authorities monitoring
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