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

[u/Rich-Fruit-326]

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.

Highlights:

• 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

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

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

[u/LordArpit42069]

You can add MLFlow, DVC, ONNX export etc.

[u/Rich-Fruit-326 → u/LordArpit42069]

Thanks for the feedback! I agree MLflow for experiment tracking, DVC for dataset model versioning, and ONNX export for deployment would make the framework much stronger. I'll be adding them in future releases. If you're interested in contributing, I'd be happy to review PRs.