Implemented and evaluated 4 machine learning models and 2 deep learning models for breast cancer classification, prioritizing recall to minimize false negatives in diagnosis. The top-performing models were fine-tuned to improve diagnostic sensitivity while maintaining strong precision and overall performance.
The final results achieved up to 98.25% accuracy, 100% precision, and reduced false negatives significantly.