Authors - Aarv Mankodi, Sanya Jain, Vedant Mundada, Dinesh Kumar Saini Abstract - Breast Cancer is a malignant tumor that occurs in the breast. It is a very serious threat to the health and well-being of women. Over the years the study of detection of this cancer using histopathology image recognition has become quite popular. Most of the methods, however, focus on creating new deep learning models or improving existing models like VGG or AlexNet. While these convolutional neural networks have been very successful in their implementation, it does not mean they do not have challenges, namely the problem of imbalance in datasets. It is for this reason that this paper instead tries to use graphs to solve the problem of breast cancer detection. For this paper, we use a graphical neural network that is trained to detect breast cancer in histopathology images. This is because the challenges faced by the deep learning models namely overcoming the imbalance in datasets may not be present in this graphical approach. It is also to find out if there are some previously unknown improvements in using a graph for detection instead of deep learning models.
