Breast cancer is the most diagnosed type of cancer as per the data that is collected by World Health Organization (WHO) within the past few years. Over 600.000 deaths were recorded in 2021 due to breast cancer. Breast cancer screening is done using 2D and 3D mammography, but MRIs and Ultrasounds are also used in certain conditions. The diagnosis from the screenings are not always accurate as a practitioner has to physically look at the digital images to find any signs of cancer. Essentially, each diagnosis has a variable chance of a false-positive or a false-negative. Many CAD (computer aided detection) systems have been developed for the assistance of a practitioner with the diagnosis. However, in the past years, Deep Neural Networks (DNN) have seen a spike and the models are being used to aid the breast cancer screening. Data shows a possibility of reaching AUC values as high as 0.99 under ideal conditions when the training dataset is cleaned of noise and properly pre-processed and in some studies, the accuracy and sensitivity is even compared to that of a practitioner’s, with the DNN model outperforming in numbers across the board. After performing a literature review on similar work, we have trained a model of our own on a publicly available dataset (MIAS) reaching promising results of an AUC 0.87 and Accuracy of 0.88 with the initial model built on a DenseNet121 architecture.