Cargando…
Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks
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 mod...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601165/ https://www.ncbi.nlm.nih.gov/pubmed/36292167 http://dx.doi.org/10.3390/diagnostics12102478 |
_version_ | 1784816990112710656 |
---|---|
author | Ji, Li Mao, Rongzhi Wu, Jian Ge, Cheng Xiao, Feng Xu, Xiaojun Xie, Liangxu Gu, Xiaofeng |
author_facet | Ji, Li Mao, Rongzhi Wu, Jian Ge, Cheng Xiao, Feng Xu, Xiaojun Xie, Liangxu Gu, Xiaofeng |
author_sort | Ji, Li |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9601165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96011652022-10-27 Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks Ji, Li Mao, Rongzhi Wu, Jian Ge, Cheng Xiao, Feng Xu, Xiaojun Xie, Liangxu Gu, Xiaofeng Diagnostics (Basel) Article 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. MDPI 2022-10-13 /pmc/articles/PMC9601165/ /pubmed/36292167 http://dx.doi.org/10.3390/diagnostics12102478 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ji, Li Mao, Rongzhi Wu, Jian Ge, Cheng Xiao, Feng Xu, Xiaojun Xie, Liangxu Gu, Xiaofeng Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks |
title | Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks |
title_full | Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks |
title_fullStr | Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks |
title_full_unstemmed | Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks |
title_short | Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks |
title_sort | deep convolutional neural network for nasopharyngeal carcinoma discrimination on mri by comparison of hierarchical and simple layered convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601165/ https://www.ncbi.nlm.nih.gov/pubmed/36292167 http://dx.doi.org/10.3390/diagnostics12102478 |
work_keys_str_mv | AT jili deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks AT maorongzhi deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks AT wujian deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks AT gecheng deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks AT xiaofeng deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks AT xuxiaojun deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks AT xieliangxu deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks AT guxiaofeng deepconvolutionalneuralnetworkfornasopharyngealcarcinomadiscriminationonmribycomparisonofhierarchicalandsimplelayeredconvolutionalneuralnetworks |