Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer
| Autor: | Szu-Tah Chen, Wai Kin Chan, Yan-Rong Li, Jui-Hung Sun, Feng-Hsuan Liu, Wei-Yu Chou, Miaw-Jene Liou, Syu-Jyun Peng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
endocrine system endocrine system diseases QH301-705.5 Medicine (miscellaneous) Convolutional neural network Article General Biochemistry Genetics and Molecular Biology Thyroid carcinoma Follicular phase thyroid cancer Medicine Biology (General) Pathological Thyroid cancer business.industry Thyroid deep learning CNNs medicine.disease artificial intelligence Image diagnosis medicine.anatomical_structure Clinical diagnosis Radiology business |
| Zdroj: | Biomedicines, Vol 9, Iss 1771, p 1771 (2021) Biomedicines Biomedicines; Volume 9; Issue 12; Pages: 1771 |
| ISSN: | 2227-9059 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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