A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account

Namdar, Khashayar and Haider, Masoom A. and Khalvati, Farzad (2021) A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account. Frontiers in Artificial Intelligence, 4. ISSN 2624-8212

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Abstract

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.

Item Type: Article
Subjects: Journal Eprints > Multidisciplinary
Depositing User: Managing Editor
Date Deposited: 17 Mar 2023 05:33
Last Modified: 10 Jul 2024 14:00
URI: http://repository.journal4submission.com/id/eprint/837

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