Lightweight Multimodal Framework Integrating Neuroimaging and Clinical Data with Explainable AI for Alzheimer's Diagnosis

A lightweight multimodal framework that integrates neuroimaging and clinical data with explainable AI for Alzheimer's diagnosis.

A lightweight multimodal framework that integrates neuroimaging and clinical data with explainable AI for Alzheimer's diagnosis. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with images or video: building a dataset, training or fine-tuning a vision model, measuring its performance and wrapping it in a usable tool.

The main parts are explanations of each result (feature importance or visual evidence), fusion of more than one type of input, dataset preparation and annotation, model training or fine-tuning, and evaluation with standard metrics, plus visual examples. The reference build uses SHAP, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.