The development of particular and effective diagnostic tools is imperative as breast cancer continues to be a major worldwide health problem. Convolutional neural networks, a form of deep learning approach, have proven wonderful overall performance in medical image evaluation responsibilities in present day years. The method provided in this chapter demonstrates CNNs’ advantages in the detection of breast cancers. For this chapter, CNN was used for diagnosing breast cancer using CBIS-DDSM (Curated Breast Imaging Subset of DDSM). The suggested technique first makes use of a CNN architecture to identify patterns that are suggestive of breast abnormalities via extracting excessive-level features from mammography images and histopathology images. These features are then extracted and fed into in default MLP classifier, which completes the very last category task and further refines the illustration. By using CNNs’ hierarchical feature learning capabilities, this method increases detection accuracy via a synergistic effect. Extensive experiments are achieved using publicly to be had datasets of images so as to assess the efficacy of the technique. The findings show that almost about breast cancers detection task, the CNN version outperforms more conventional machine learning algorithms in phrases of accuracy, sensitivity, and specificity.