“…Owing to the strong representation ability, the DCNN-based methods can generally perform the classification task effectively. In References [ 15 , 16 ], data imbalance is addressed with the cost-sensitive loss and Area Under the receiver operating characteristics Curve (AUC) statistics, which is directly derived from the statistic information of the input dataset. In References [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], data-level methods, algorithm-level methods, and hybrid methods were employed to address the data imbalance, boosting the performance to some extent.…”