This paper proposes a novel approach using deep convolutional generative adversarial networks (DCGANs) to generate synthetic mammograms, addressing the challenge of limited annotated data for breast cancer diagnosis. The goal is to create synthetic mammograms that accurately replicate the intrinsic patterns of real data, thereby enhancing the current dataset. The proposed method involves generating synthetic images, evaluating their similarity to real data, and removing outliers to improve the reliability of the synthetic dataset. The study demonstrates that the generated images closely resemble real mammograms, with high similarity scores and minimal deviation from the mean values of each class. The synthetic images were validated by human experts, who confirmed their clinical relevance and diagnostic accuracy. The results show that the DCGAN-based approach effectively generates high-quality synthetic mammograms, which can be used to improve the accuracy of deep learning models for breast cancer detection. The methodology includes data collection from the DDSM dataset, data preprocessing, DCGAN architecture design, training process, and performance evaluation. The study highlights the effectiveness of DCGANs in generating realistic mammograms and their potential to enhance breast cancer diagnosis through improved data augmentation and validation. The results indicate that the proposed method significantly improves the quality and reliability of synthetic mammograms, making them suitable for clinical applications. The study also emphasizes the importance of statistical validation and outlier removal in ensuring the authenticity and consistency of the generated data. The findings suggest that DCGANs can be a valuable tool in addressing data scarcity in breast cancer diagnosis and improving the accuracy of deep learning models for medical imaging.This paper proposes a novel approach using deep convolutional generative adversarial networks (DCGANs) to generate synthetic mammograms, addressing the challenge of limited annotated data for breast cancer diagnosis. The goal is to create synthetic mammograms that accurately replicate the intrinsic patterns of real data, thereby enhancing the current dataset. The proposed method involves generating synthetic images, evaluating their similarity to real data, and removing outliers to improve the reliability of the synthetic dataset. The study demonstrates that the generated images closely resemble real mammograms, with high similarity scores and minimal deviation from the mean values of each class. The synthetic images were validated by human experts, who confirmed their clinical relevance and diagnostic accuracy. The results show that the DCGAN-based approach effectively generates high-quality synthetic mammograms, which can be used to improve the accuracy of deep learning models for breast cancer detection. The methodology includes data collection from the DDSM dataset, data preprocessing, DCGAN architecture design, training process, and performance evaluation. The study highlights the effectiveness of DCGANs in generating realistic mammograms and their potential to enhance breast cancer diagnosis through improved data augmentation and validation. The results indicate that the proposed method significantly improves the quality and reliability of synthetic mammograms, making them suitable for clinical applications. The study also emphasizes the importance of statistical validation and outlier removal in ensuring the authenticity and consistency of the generated data. The findings suggest that DCGANs can be a valuable tool in addressing data scarcity in breast cancer diagnosis and improving the accuracy of deep learning models for medical imaging.