Civilization is witnessing a continuous development in human life and society, the medical field is constantly improving. However, modern medicine still faces many limitations, including challenging and previously unsolvable problems. One of the most common challenge in the medical field is the scarce of data. With the Evolution of Artificial Intelligence and its impact on various fields including medical diagnosis, it's important to benefit from AI characteristics to improve the effectiveness of medical diagnosis, We Present Generative adversarial network (GAN) as one of most relevant and effective method for data augmentation that can help us to generate realistic data that resemble the original data. We use an improved GAN architecture that involves Convolutional Neural Network (CNN) to improve the quality of generated images called Deep Convolutional Generative Adversarial Network (DCGAN). The model can learn the distribution of data and generate realistic synthetic data with a similar distribution of the original data allowing the ability to extend the dataset with entirely new data. For evaluation we use Inception score (IS), Higher IS values indicate a high quality and diversity. Our model shows a promising results and generate realistic data that we can use to train machine learning and deep learning models.