<![CDATA[One of the major challenges in medical imaging is the limited availability of high-quality datasets. To address this, Generative Artificial Intelligence (Generative AI) offers a promising solution by generating synthetic medical images to augment existing datasets. This study explores the application of Deep Convolutional Generative Adversarial Networks (DCGAN) for data augmentation in CIN imaging. Two training scenarios were implemented: DCGAN with manual data augmentation and another without manual augmentation. The image quality was evaluated using the Fréchet Inception Distance (FID). The results indicate that incorporating data augmentation improves the stability of training and enhances the quality of generated images FID scores of 2.21. In contrast, training DCGAN without manual augmentation resulted in a higher FID score of 2.52, indicating lower image quality. These findings highlight the effectiveness of DCGAN in medical image augmentation and its potential to enhance deep learning-based diagnostic models for cervical cancer detection or classification.]]>
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