Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer
Differentiated thyroid cancer (DTC) from follicular epithelial cells is the most common form of thyroid cancer. Beyond the common papillary thyroid carcinoma (PTC), there are a number of rare but difficult-to-diagnose pathological classifications, such as follicular thyroid carcinoma (FTC). We emplo...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/9/12/1771 |
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| author | Wai-Kin Chan Jui-Hung Sun Miaw-Jene Liou Yan-Rong Li Wei-Yu Chou Feng-Hsuan Liu Szu-Tah Chen Syu-Jyun Peng |
| author_facet | Wai-Kin Chan Jui-Hung Sun Miaw-Jene Liou Yan-Rong Li Wei-Yu Chou Feng-Hsuan Liu Szu-Tah Chen Syu-Jyun Peng |
| author_sort | Wai-Kin Chan |
| collection | DOAJ |
| description | Differentiated thyroid cancer (DTC) from follicular epithelial cells is the most common form of thyroid cancer. Beyond the common papillary thyroid carcinoma (PTC), there are a number of rare but difficult-to-diagnose pathological classifications, such as follicular thyroid carcinoma (FTC). We employed deep convolutional neural networks (CNNs) to facilitate the clinical diagnosis of differentiated thyroid cancers. An image dataset with thyroid ultrasound images of 421 DTCs and 391 benign patients was collected. Three CNNs (InceptionV3, ResNet101, and VGG19) were retrained and tested after undergoing transfer learning to classify malignant and benign thyroid tumors. The enrolled cases were classified as PTC, FTC, follicular variant of PTC (FVPTC), Hürthle cell carcinoma (HCC), or benign. The accuracy of the CNNs was as follows: InceptionV3 (76.5%), ResNet101 (77.6%), and VGG19 (76.1%). The sensitivity was as follows: InceptionV3 (83.7%), ResNet101 (72.5%), and VGG19 (66.2%). The specificity was as follows: InceptionV3 (83.7%), ResNet101 (81.4%), and VGG19 (76.9%). The area under the curve was as follows: Incep-tionV3 (0.82), ResNet101 (0.83), and VGG19 (0.83). A comparison between performance of physicians and CNNs was assessed and showed significantly better outcomes in the latter. Our results demonstrate that retrained deep CNNs can enhance diagnostic accuracy in most DTCs, including follicular cancers. |
| first_indexed | 2024-03-10T04:33:44Z |
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| id | doaj.art-6ff5ecddb8634fb98901e35e38a503cc |
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| language | English |
| last_indexed | 2024-03-10T04:33:44Z |
| publishDate | 2021-11-01 |
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| series | Biomedicines |
| spelling | doaj.art-6ff5ecddb8634fb98901e35e38a503cc2023-11-23T03:55:18ZengMDPI AGBiomedicines2227-90592021-11-01912177110.3390/biomedicines9121771Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid CancerWai-Kin Chan0Jui-Hung Sun1Miaw-Jene Liou2Yan-Rong Li3Wei-Yu Chou4Feng-Hsuan Liu5Szu-Tah Chen6Syu-Jyun Peng7Division of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 33302, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 33302, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 33302, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 33302, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 33302, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 33302, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 33302, TaiwanProfessional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 10675, TaiwanDifferentiated thyroid cancer (DTC) from follicular epithelial cells is the most common form of thyroid cancer. Beyond the common papillary thyroid carcinoma (PTC), there are a number of rare but difficult-to-diagnose pathological classifications, such as follicular thyroid carcinoma (FTC). We employed deep convolutional neural networks (CNNs) to facilitate the clinical diagnosis of differentiated thyroid cancers. An image dataset with thyroid ultrasound images of 421 DTCs and 391 benign patients was collected. Three CNNs (InceptionV3, ResNet101, and VGG19) were retrained and tested after undergoing transfer learning to classify malignant and benign thyroid tumors. The enrolled cases were classified as PTC, FTC, follicular variant of PTC (FVPTC), Hürthle cell carcinoma (HCC), or benign. The accuracy of the CNNs was as follows: InceptionV3 (76.5%), ResNet101 (77.6%), and VGG19 (76.1%). The sensitivity was as follows: InceptionV3 (83.7%), ResNet101 (72.5%), and VGG19 (66.2%). The specificity was as follows: InceptionV3 (83.7%), ResNet101 (81.4%), and VGG19 (76.9%). The area under the curve was as follows: Incep-tionV3 (0.82), ResNet101 (0.83), and VGG19 (0.83). A comparison between performance of physicians and CNNs was assessed and showed significantly better outcomes in the latter. Our results demonstrate that retrained deep CNNs can enhance diagnostic accuracy in most DTCs, including follicular cancers.https://www.mdpi.com/2227-9059/9/12/1771thyroid cancerartificial intelligencedeep learningCNNs |
| spellingShingle | Wai-Kin Chan Jui-Hung Sun Miaw-Jene Liou Yan-Rong Li Wei-Yu Chou Feng-Hsuan Liu Szu-Tah Chen Syu-Jyun Peng Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer Biomedicines thyroid cancer artificial intelligence deep learning CNNs |
| title | Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer |
| title_full | Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer |
| title_fullStr | Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer |
| title_full_unstemmed | Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer |
| title_short | Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer |
| title_sort | using deep convolutional neural networks for enhanced ultrasonographic image diagnosis of differentiated thyroid cancer |
| topic | thyroid cancer artificial intelligence deep learning CNNs |
| url | https://www.mdpi.com/2227-9059/9/12/1771 |
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