Revolutionizing Brain Tumour Prediction: A Pioneering GAN-based Framework for Synthetic Data Generation

Medical analysis is a key component of contemporary healthcare since it helps doctors diagnose patients accurately to plan and track their treatments. Accurate identification of brain tumors is essential for doctors to treat patients swiftly and efficiently. This research investigates a novel approach to overcome the constraints imposed by scarce and sensitive medical data, generating synthetic images of brain tumors using Generative Adversarial Networks (GANs). The proposed method has the potential to enhance activities involving medical processing, which will aid in making diagnoses and formulating treatment plans. The preliminary findings demonstrate its potential relevance to a wider variety of medical processing tasks, demonstrating that this augmentation method yields significant benefits.