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Rapid diagnosis of infectious diseases, such as monkeypox, is essential for effective containment and treatment, particularly in resource-constrained settings. This study presents an AI-driven diagnostic tool optimized for deployment on the NVIDIA Jetson Orin Nano. Several pre-trained architectures were evaluated, with MobileNetV2 and DenseNet121 achieving the best F1-scores of 91.87% and 86.70%, respectively, on the Monkeypox Skin Lesion Dataset (MSLD and MSLD v2.0). TensorRT was used for model optimization, leveraging FP32, FP16, and INT8 precision formats to accelerate inference while reducing model size and power consumption. Results show up to 2.52 \(\times \) speedup and improved energy efficiency with minimal accuracy loss. The system was deployed with a Wi-Fi Access Point (AP) hotspot and a web-based interface, enabling users to upload and analyze images directly through connected devices such as mobile phones. These advancements position the tool as a scalable, efficient, and low-power diagnostic solution for deployment in underserved healthcare settings.

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Computer Vision for Real-Time Monkeypox Diagnosis on Embedded Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Jacob M. Delgado-López,
  • Ricardo A Morell-Rodriguez,
  • Sebastián O Espinosa-Del Rosario,
  • Wilfredo E. Lugo-Beauchamp

摘要

Rapid diagnosis of infectious diseases, such as monkeypox, is essential for effective containment and treatment, particularly in resource-constrained settings. This study presents an AI-driven diagnostic tool optimized for deployment on the NVIDIA Jetson Orin Nano. Several pre-trained architectures were evaluated, with MobileNetV2 and DenseNet121 achieving the best F1-scores of 91.87% and 86.70%, respectively, on the Monkeypox Skin Lesion Dataset (MSLD and MSLD v2.0). TensorRT was used for model optimization, leveraging FP32, FP16, and INT8 precision formats to accelerate inference while reducing model size and power consumption. Results show up to 2.52 \(\times \) speedup and improved energy efficiency with minimal accuracy loss. The system was deployed with a Wi-Fi Access Point (AP) hotspot and a web-based interface, enabling users to upload and analyze images directly through connected devices such as mobile phones. These advancements position the tool as a scalable, efficient, and low-power diagnostic solution for deployment in underserved healthcare settings.