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

Hi everyone,

We've open-sourced Tri-Net v2, the official implementation accompanying our recently published Scientific Reports (Nature Portfolio) paper: "Tri-Net: Unified Deep Learning for Skin Lesion and Symptom-Based Monkeypox Detection". Rather than releasing only training scripts, we rebuilt the project as a reproducible research framework.

Key Features

• Leakage-free data preparation pipeline
• Multiple CNN backbones (ConvNeXt-Tiny, DenseNet201, Inception-ResNetV2)
• Ensemble and feature-fusion strategies
• Grad-CAM explainability
• Cross-validation and statistical evaluation
• Docker support
• GitHub Actions CI
• PyPI package (pip install mpox-trinet)
• CLI for training, inference, and benchmarking

Paper and Resources

The paper has already received over 1,100 article accesses in its first week, and we hope making the implementation fully open-source will help others reproduce, validate, and extend the work.

• GitHub: https://github.com/Sudharsanselvaraj/Synergistic-Deep-Learning-for-Monkeypox-Diagnosis
• PyPI: https://pypi.org/project/Mpox-Trinet/
• Paper: https://www.nature.com/articles/s41598-026-61490-x

Feedback

I'd really appreciate feedback on the implementation, reproducibility, code quality, or ideas for future improvements. Contributions and issues are very welcome!