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Gal, Y. and Ghahramani, Z. (2016) Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. Proceedings of the 33rd International Conference on Machine Learning, Vol. 48, 1050-1059.

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• TITLE: Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images

  AUTHORS: Eri Matsuyama, Masayuki Nishiki, Noriyuki Takahashi, Haruyuki Watanabe

  KEYWORDS: Cross Entropy, Performance Metrics, DNN Image Classifiers, Lung Cancer, Prediction Uncertainty

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.17 No.1, January 17, 2024

  ABSTRACT: Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential.