Enhancing Grayscale Image Synthesis with Deep Conditional GAN and Transfer Learning

This study explores using advanced Generative Adversarial Networks (GAN) to create realistic breast ultrasound images. Combining Deep Convolutional GAN with a Wasserstein Gradient-Penalty and integrating Transfer Learning aims to address the lack of diverse datasets in breast cancer diagnosis. The research proposes a new method to generate high-quality images of breast tumors, which involves using state-of-the-art DCGAN-WG-TL models with pre-trained networks like VGG19 to refine the synthetic image generation process. The methodology includes preprocessing the Breast Ultrasound Images dataset, generating synthesized images, and evaluating the results using the Fréchet Inception Distance (FID) metric to assess image quality. The study found that DCGAN-WG-TL with lambda = 5.0 and 500 epochs produced the best results.