Breast Cancer Detection Using a Hybrid 1-D CNN and XGBoost Algorithm

A breast cancer detection model that combines a hybrid 1-D CNN with XGBoost.

A breast cancer detection model that combines a hybrid 1-D CNN with XGBoost. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend 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 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 XGBoost, Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.