Author(s)

Eri Matsuyama¹, Masayuki Nishiki², Noriyuki Takahashi³, Haruyuki Watanabe⁴

¹Faculty of Informatics, University of Fukuchiyama, Kyoto, Japan.
²Graduate School of Radiological Sciences, International University of Health and Welfare, Tochigi, Japan.
³School of Health Sciences, Fukushima Medical University, Fukushima, Japan.
⁴School of Radiological Technology, Gunma Prefectural College of Health Sciences, Gunma, Japan.

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.

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

Matsuyama, E. , Nishiki, M. , Takahashi, N. and Watanabe, H. (2024) Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images. Journal of Biomedical Science and Engineering, 17, 1-12. doi: 10.4236/jbise.2024.171001.