Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network
The classification of benign and malignant parotid tumors is very crucial for the selection of surgical methods and their prognoses. The wide application of deep learning technology in the field of medical imaging also provides new ideas for the computer-aided diagnosis of parotid gland tumors. In a...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9373340/ |
| _version_ | 1818616609247330304 |
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| author | Hongbin Zhang Huicheng Lai Yan Wang Xiaoyi Lv Yue Hong Jianming Peng Ziwei Zhang Chen Chen Cheng Chen |
| author_facet | Hongbin Zhang Huicheng Lai Yan Wang Xiaoyi Lv Yue Hong Jianming Peng Ziwei Zhang Chen Chen Cheng Chen |
| author_sort | Hongbin Zhang |
| collection | DOAJ |
| description | The classification of benign and malignant parotid tumors is very crucial for the selection of surgical methods and their prognoses. The wide application of deep learning technology in the field of medical imaging also provides new ideas for the computer-aided diagnosis of parotid gland tumors. In addition, because the pathological types of parotid gland tumors are very complicated and the computed tomography (CT) images of benign and malignant patients are also very similar, some clinicians may misjudge tumors due to a lack of experience, which affects the effect of surgical treatment and prognosis. Therefore, this research proposes using deep learning methods to solve this problem. This study uses the four classic pretraining models of VGG16, InceptionV3, ResNet and DenseNet to classify parotid CT images using transfer learning methods and uses an improved convolutional neural network (CNN) model to classify parotid CT images. The experimental results show that the improved CNN model achieves an accuracy of 97.78%, and its classification performance is better than those of the other four transfer learning methods. It can effectively diagnose benign and malignant parotid tumors and improve the diagnostic accuracy. |
| first_indexed | 2024-12-16T16:52:31Z |
| format | Article |
| id | doaj.art-a4053c68586a4a1c9c649d277a42739d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:52:31Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4053c68586a4a1c9c649d277a42739d2022-12-21T22:23:59ZengIEEEIEEE Access2169-35362021-01-019403604037110.1109/ACCESS.2021.30647529373340Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural NetworkHongbin Zhang0https://orcid.org/0000-0003-1359-8825Huicheng Lai1Yan Wang2Xiaoyi Lv3https://orcid.org/0000-0001-6855-7428Yue Hong4Jianming Peng5Ziwei Zhang6Chen Chen7https://orcid.org/0000-0003-1406-5721Cheng Chen8https://orcid.org/0000-0002-6739-1937College of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaRadiology Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, ChinaCollege of Software, Xinjiang University, Ürümqi, ChinaRadiology Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, ChinaDepartment of Information, People’s Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaThe classification of benign and malignant parotid tumors is very crucial for the selection of surgical methods and their prognoses. The wide application of deep learning technology in the field of medical imaging also provides new ideas for the computer-aided diagnosis of parotid gland tumors. In addition, because the pathological types of parotid gland tumors are very complicated and the computed tomography (CT) images of benign and malignant patients are also very similar, some clinicians may misjudge tumors due to a lack of experience, which affects the effect of surgical treatment and prognosis. Therefore, this research proposes using deep learning methods to solve this problem. This study uses the four classic pretraining models of VGG16, InceptionV3, ResNet and DenseNet to classify parotid CT images using transfer learning methods and uses an improved convolutional neural network (CNN) model to classify parotid CT images. The experimental results show that the improved CNN model achieves an accuracy of 97.78%, and its classification performance is better than those of the other four transfer learning methods. It can effectively diagnose benign and malignant parotid tumors and improve the diagnostic accuracy.https://ieeexplore.ieee.org/document/9373340/Parotid gland tumordeep learningCT imagetransfer learningconvolutional neural network |
| spellingShingle | Hongbin Zhang Huicheng Lai Yan Wang Xiaoyi Lv Yue Hong Jianming Peng Ziwei Zhang Chen Chen Cheng Chen Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network IEEE Access Parotid gland tumor deep learning CT image transfer learning convolutional neural network |
| title | Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network |
| title_full | Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network |
| title_fullStr | Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network |
| title_full_unstemmed | Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network |
| title_short | Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network |
| title_sort | research on the classification of benign and malignant parotid tumors based on transfer learning and a convolutional neural network |
| topic | Parotid gland tumor deep learning CT image transfer learning convolutional neural network |
| url | https://ieeexplore.ieee.org/document/9373340/ |
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