The accurate detection of breast cancer plays a vital role in improving patient outcomes and reducing mortality rates. To address this critical need, a Deep Learning technique based on Convolutional Neural Networks (CNNs) was proposed to enhance the predictive model for breast cancer identification. This model focuses on classifying mammogram images as either benign or malignant. The study utilized a diverse dataset of mammogram images, encompassing various cases, which were partitioned for training, validation, and testing purposes. The CNN architecture was employed to construct the predictive model, leveraging its ability to learn intricate patterns and features indicative of breast cancer. The implementation of advanced machine learning models for breast cancer detection contributes to the medical field's ability to deliver precise diagnoses and improve patient outcomes. By leveraging the capabilities of CNNs, this model aids in the early detection of breast cancer, potentially saving lives and positively impacting individuals affected by the disease.

CNN, mammogram, benign, malignant , Artificial Intelligence, Deep Learning