 Invasive ductal carcinoma breast cancer, IDCBC, is the most common type of cancer and it's a symptomatic nature has led to an increased mortality rate globally. Advancements in artificial intelligence and machine learning have revolutionized the medical field with the development of AI enabled computer aided diagnosis, CAD, systems, which help in determining diseases at an early stage. These systems assist pathologists in their decision-making process to produce more reliable outcomes in order to treat patients well. Pre-trained convolutional neural networks, CNNs, such as EfficientNet V2L, ResNet 152V2, and DenseNet 201, were explored individually and as an ensemble to classify IDCBC grades from the data biogs dataset. Data augmentation was used to avoid the issues of data scarcity and data imbalances. The performance of the best model was compared to three different balanced datasets of data biogs, 1200, 1400, and 1600 images. Additionally, the effects of the number of epochs were analyzed to ensure the coherency. This article was authored by Elangela Kumar Swamy, Simit Kumar, and Manoj Sharma. We are article.tv, links in the description below.