Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information

Abstract

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 attention-based model TabNet based on the domain of breast cancer research at every stage of the research framework. The research findings are justified using benchmark breast cancer datasets. After rigorous investigation, it was found that the TVAE model outperformed the synthetic generation of breast tumor data with a Chi-Squared test(CS test) score of 0.916 (prognosis) and 0.964 (diagnosis) and a Kolmogorov Smirnov test(KS test) score of 0.887 (prognosis) and 0.928 (diagnosis). In the classification stage, despite being trained with only synthetically generated data, the interpretable TabNet architecture outperformed all other machine-learning and deep-learning classifiers with an accuracy of 96.66 % in diagnosis and 82.83 % in prognosis.