Multimodal AI Framework for Skin Disease Detection Using YOLO-Based Lesion Localization and BERT-Based Clinical Text Analysis

A multimodal AI framework for skin disease detection that uses YOLO-based lesion localisation and BERT-based clinical text analysis.

A multimodal AI framework for skin disease detection that uses YOLO-based lesion localisation and BERT-based clinical text analysis. 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 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 fusion of more than one type of input, dataset preparation and annotation, model training or fine-tuning, evaluation with standard metrics, plus visual examples, and an interface that works on uploaded images or live video. The reference build uses YOLO, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.