Leveraging Bioinformatics for Enhanced Breast CancerPrediction Using CNN-Based Deep Learning Models

Breast cancer continues to be a leading cause of death for women globally, highlighting the need for timely and precise identification to enhance patient survival rates. The emergence of advanced deep learning algorithms has transformed the healthcare industry by offering effective resources for medical diagnosis. This research explores the application of “Convolutional Neural Networks (CNN)” in predicting breast cancer, leveraging feature selection to enhance model performance. “Deep Learning (DL)” models, with their ability to automatically extract and learn complex patterns from large datasets, offer significant advantages over traditional methods. These include higher accuracy, reduced need for manual feature extraction, and the potential for continuous improvement as more data becomes available. By incorporating feature selection and normalization techniques, we demonstrate how a CNN can effectively classify malignant and benign breast tumors, achieving robust performance metrics.