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Breast cancer is the world’s second-largest cause of cancer mortality among women. With the progress of artificial intelligence (AI) in healthcare, the survival rate of breast cancer patients has risen in recent years due to early diagnosis and effective prognosis. However, substantial AI research necessitates a large quantity of high-quality data to perform credible state-of-the-art research. To that end, this study investigates the potentiality of deep generative models including, the tabular variational autoencoder (TVAE) and the conditional generative adversarial network (CTGAN), to generate high-quality synthetic tabular data of breast tumors and support the diagnosis and prognosis of breast cancer. Additionally, this study proposes an integrated interpretable deep-learning framework that includes the synthetic generation of breast cancer data leading to the classification of breast cancer using the interpretable deep …