Enhancing Breast Cancer Diagnosis Through Segmentation-Driven Generative Adversarial Networks for Synthetic Mammogram Generation

Breast cancer remains a global health challenge, necessitating innovative diagnostic strategies for early detection and precise treatment. This paper introduces a pioneering approach that leverages the transformative capabilities of Generative Adversarial Networks (GANs) to advance breast cancer diagnosis through Segmentation-Driven Synthetic Mammogram Generation. By seamlessly integrating accurate segmentation algorithms and generative AI, our framework addresses the scarcity of annotated medical images and enhances diagnostic accuracy. Synthetic mammograms, faithfully emulating real-world scenarios, are generated to enrich the training dataset, fostering a diversified learning environment for diagnostic models. This synergy of segmentation and synthesis not only empowers clinicians with a broader exposure to cases but also fuels the development of robust diagnostic models capable of tackling clinical challenges. Through an interdisciplinary lens, our approach ushers in a new era in medical imaging, illuminating a path toward improved patient outcomes and reshaping the landscape of breast cancer diagnosis. This paper paves the way for transformative advancements at the intersection of AI -driven image synthesis and clinical medicine.

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