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.

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!