Depression Detection Using Multimodal Feature Fusion with Cross-Attention

A deep learning model that detects depression by fusing signals from several modalities using cross-attention.

A deep learning model that detects depression by fusing signals from several modalities using cross-attention. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are fusion of more than one type of input, dataset preparation and augmentation, model design and training, evaluation with standard metrics, plus error analysis, and a simple interface or API to try the model. The reference build uses Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.