Resilient Breast Cancer Detection and Accurate Tumor Region Localization using a Robust Deep Learning Framework

Detection of breast cancer is, in fact, one of the most important areas of research in studies concerning medical imaging, in which early detection improves survival rates and cuts treatment costs by a significant margin. The GAN-based system for the detection of breast cancer described here attempts to overcome problems in respect of accuracy, sensitivity, and specificity found in diagnostic models reported earlier. Conventional techniques for breast cancer detection suffer from issues such as class imbalance and limited datasets that lead to suboptimal performance in many cases. The proposed framework implements GAN to create synthetic data that helps augment the training datasets and enhance the robustness of models. It features a generated two-stage architecture where, with the help of a generator, realistic synthetic mammogram images are created in order to balance the class distribution, and the discriminator will segment both real and synthetic images effectively. This model will help overcome the problems with data scarcity and hence its performance on generalizing the model to unseen data.