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Breast cancer has the highest incidence and death rate among women; moreover, its metastasis to other organs increases the mortality rate. Since several studies have reported gene expression and cancer prognosis to be related, the study of breast cancer metastasis using gene expression is crucial. To this end, a novel deep neural network architecture, deep learning-based cancer metastasis estimator (DeepCME), is proposed in this paper for predicting breast cancer metastasis. However, the problem of overfitting occurs frequently while training deep learning models using gene expression data because they contain a large number of genes and the sample size is rather small. To address overfitting, several regularization methods are implemented, such as L1 penalty, batch normalization, and dropout. To demonstrate the superior performance of our model, area under curve (AUC) scores are evaluated and …