  Since technology is advancing so quickly in the modern era of information,
data is becoming an essential resource in many fields. Correct data collection,
organization, and analysis make it a potent tool for successful
decision-making, process improvement, and success across a wide range of
sectors. Synthetic data is required for a number of reasons, including the
constraints of real data, the expense of collecting labeled data, and privacy
and security problems in specific situations and domains. For a variety of
reasons, including security, ethics, legal restrictions, sensitivity and
privacy issues, and ethics, synthetic data is a valuable tool, particularly in
the health sector. A deep learning model called GAN (Generative Adversarial
Networks) has been developed with the intention of generating synthetic data.
In this study, the Breast Histopathology dataset was used to generate malignant
and negatively labeled synthetic patch images using MSG-GAN (Multi-Scale
Gradients for Generative Adversarial Networks), a form of GAN, to aid in cancer
identification. After that, the ResNet18 model was used to classify both
synthetic and real data via Transfer Learning. Following the investigation, an
attempt was made to ascertain whether the synthetic images behaved like the
real data or if they are comparable to the original data.
