Abstract

This study presents a hybrid artificial intelligence model for the early detection of breast cancer through the integration of imaging, genetic, and clinical data. The proposed model combines convolutional neural networks for image analysis with machine learning algorithms for processing genetic markers and clinical variables. The research evaluates the model's performance on benchmark datasets and demonstrates superior accuracy, sensitivity, and specificity compared to single-modality approaches. The findings suggest that multi-modal AI integration holds significant promise for improving early diagnosis and ultimately reducing breast cancer mortality rates.