Implementing Generative Adversarial Networks and Cloud Services for Identifying Breast Cancer in Healthcare Systems

Abstract

The study primarily aims at investigating the deployment of GAN and cloud services in the identification of breast cancer. Providing a method of deep learning models integrated into cloud computing offers processing power and storage facilities on scalable and on-demand bases; it is the most critical solution when considering the huge bulk of medical data accumulated in healthcare systems and IoT-wearables. The research emphasizes that preprocessing methods, such as data normalization and augmentation, will improve the GAN model's efficiency. The features are extracted by Autoencoders and then classified via GANs for breast-cancer-image classification. This model has features like easy accessibility, hosted on-cloud, and scalable for deployment. Accuracy, precision, recall, and F1 score are among the used performance metrics to evaluate the effectiveness of the model. The results indicate high classification accuracy and robustness of the model, suggesting potential of employing cloud-based GANs for the breast cancer detection clinical settings.