Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks

Li Ji; Rongzhi Mao; Jian Wu; Cheng Ge; Feng Xiao; Xiaojun Xu; Liangxu Xie; Xiaofeng Gu

doi:10.3390/diagnostics12102478

Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks

Diagnostics (Basel). 2022 Oct 13;12(10):2478. doi: 10.3390/diagnostics12102478.

Authors

Li Ji¹, Rongzhi Mao², Jian Wu¹, Cheng Ge², Feng Xiao¹, Xiaojun Xu², Liangxu Xie², Xiaofeng Gu¹

Affiliations

¹ Department of Otorhinolaryngology, The Second People's Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China.
² Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China.

Abstract

Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.

Keywords: artificial intelligence; convolutional neural network; deep learning; image diagnosis; nasopharyngeal carcinoma.

Grants and funding

This work was supported by the Natural Science Foundation of Jiangsu Province (BK20191032), Changzhou Sci. & Tech. Program (CJ20200045), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX22_1480).