Abstract: One of the significant contributors to the present death rate for women is breast cancer. An earlier diagnosis of this illness can save costs and increase survival rates. In the past, machine learning algorithms that were used to categorize data were only dependent on manually created features, which could not adequately capture variations and needed a higher degree of classification accuracy. The classification problem was also solved using deep learning methods. The experiment seeks to categorize the cancerous cell types — benign and malignant - using a convolutional neural network (CNN) based model. It is the most incredible technique for classifying images since it is one of the best at identifying the items and patterns in an image. It is a that have a kind of deep learning approach that is best known for spotting patterns in images; it aims to present images in an abstract form that contains the most soundless information required for differentiating them from other images with a similar appearance. BreakHis, a dataset with microscopic biopsy images, and BUSI, a dataset with histopathology images, were employed for this investigation. The primary goal of this experiment is to experiment with the model used to diagnose breast cancer by categorizing various breast photos as either cancerous (Malignant) or non-cancerous (Benign).

Key words: neural networks, patch-based classification, deep learning, transfer learning, breast cancer