Breast cancer is one of the most significant issues today. Doctors use methods like mammograms for its detection. Early recognition of this disease is crucial, so automation using convolutional neural networks is proposed, classifying mammograms according to BI-RADS categories. The proposed methodology seeks to streamline image analysis through the automation capabilities of convolutional neuronal networks, presenting a swifter and more reliable alternative to manual assessment. Multiple class distributions were conducted on the dataset to enhance the model’s performance. These divisions provided a deeper understanding of the model and facilitated the identification of optimal parameter combinations and strategies to optimize its effectiveness. Experimental findings affirm the method’s proficiency in accurately classifying mammography images according to BI-RADS standards. This innovative approach holds promise for the development of automated systems to aid radiologists in the early detection of breast cancer.