  Receiver operating characteristic (ROC) curve is an informative tool in
binary classification and Area Under ROC Curve (AUC) is a popular metric for
reporting performance of binary classifiers. In this paper, first we present a
comprehensive review of ROC curve and AUC metric. Next, we propose a modified
version of AUC that takes confidence of the model into account and at the same
time, incorporates AUC into Binary Cross Entropy (BCE) loss used for training a
Convolutional neural Network for classification tasks. We demonstrate this on
three datasets: MNIST, prostate MRI, and brain MRI. Furthermore, we have
published GenuineAI, a new python library, which provides the functions for
conventional AUC and the proposed modified AUC along with metrics including
sensitivity, specificity, recall, precision, and F1 for each point of the ROC
curve.
