Tri-Net v2: Open-source implementation of our Scientific Reports paper on unified skin lesion and symptom-based monkeypox detection [R]

Short summary

Researchers released Tri-Net v2, an open-source deep learning framework for monkeypox detection from skin lesion images and symptoms, accompanying their paper published in Scientific Reports (Nature Portfolio). The implementation includes leakage-free data pipelines, multiple CNN backbones with ensemble strategies, Grad-CAM explainability, Docker support, and a PyPI package for easy installation. The project emphasizes reproducibility with cross-validation, statistical evaluation, and CI/CD integration.

• •Open-sourced Tri-Net v2: a reproducible deep learning framework for monkeypox detection from skin lesions and symptoms
• •Supports multiple CNN backbones (ConvNeXt-Tiny, DenseNet201, Inception-ResNetV2) with ensemble fusion and Grad-CAM explainability
• •Available on GitHub and PyPI with Docker support and CI; paper published in Scientific Reports with 1,100+ accesses in first week

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