Enhancing the Quality and Authenticity of Synthetic Mammogram Images for Improved Breast Cancer Detection

Abstract

Breast cancer is widespread worldwide and can be cured if diagnosed early.Mammography is an irreplaceable and critical technique in modern medicine, serving as a foundation for breast cancer detection. In medical imaging, the reliability of synthetic mammogram images is produced by Deep Convolutional Generative Adversarial Networks (DCGANs). The human validation for assessing the quality of synthetic images for examining and calculating the perceptual variations between synthetic images and real-world counterparts is a difficult task. Thus, this research focused on improving the quality and authenticity of synthetic mammogram images. For this, we explored and identified a new research gap because the radiologists consistently expressed much higher confidence levels in real mammogram images in their assessment process. This research highlights the key difference between synthetic and real mammograms by defining mean scores. The defined mean identifies a sizable gap, with real mammographic images receiving an average score of 0.73 and a synthetic score of 0.31. we performed a statistical analysis, which yielded a T-statistic of -6.35, a p-value less than 0.001, and a 95% confidence interval ranging from -0.50 to -0.28. These results have a wide range of ramifications. It emphasizes the urgent need for further generative model improvement, improving the legitimacy and caliber of synthetic mammogram images. Our research highlights how crucial it is to incorporate synthetic images into clinical practice with caution and thought. The Ethical considerations must encompass the potential consequences of relying on synthetic data in medical decision-making, alongside concerns related to diagnostic accuracy and patient safety. In future research, we work on improving generative models, leveraging more extensive and more diverse datasets. Comprehensive validation studies, encompassing a broader spectrum of radiologists and datasets, are pivotal to validate and generalize our findings. This study sets the stage for a deeper understanding of the validity of synthetic mammogram images.