Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules

Objective. To investigate the application value of a deep convolutional neural network (CNN) model for cytological assessment of thyroid nodules. Methods. 117 patients with thyroid nodules who underwent thyroid cytology examination in the Affiliated People’s Hospital of Ningbo University between Jan...

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Autores principales:	Ying Ren, Yu He, Linghua Cong
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/af2cb688be9e49eca7fa5068d468d5a6
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spelling	oai:doaj.org-article:af2cb688be9e49eca7fa5068d468d5a62021-11-22T01:10:33ZApplication Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules2040-230910.1155/2021/6076135https://doaj.org/article/af2cb688be9e49eca7fa5068d468d5a62021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6076135https://doaj.org/toc/2040-2309Objective. To investigate the application value of a deep convolutional neural network (CNN) model for cytological assessment of thyroid nodules. Methods. 117 patients with thyroid nodules who underwent thyroid cytology examination in the Affiliated People’s Hospital of Ningbo University between January 2017 and December 2019 were included in this study. 100 papillary thyroid cancer samples and 100 nonmalignant samples were collected respectively. The sample images were translated vertically and horizontally. Thus, 900 images were separately created in the vertical and horizontal directions. The sample images were randomly divided into training samples (n = 1260) and test samples (n = 540) at the ratio of 7 : 3 per the training sample to test sample. According to the training samples, the pretrained deep convolutional neural network architecture Resnet50 was trained and fine-tuned. A convolutional neural network-based computer-aided detection (CNN-CAD) system was constructed to perform full-length scan of the test sample slices. The ability of CNN-CAD to screen malignant tumors was analyzed using the threshold setting method. Eighty pathological images were collected from patients who received treatment between January 2020 and May 2020 and used to verify the value of CNN in the screening of malignant thyroid nodules as verification set. Results. With the number of iterations increasing, the training and verification loss of CNN model gradually decreased and tended to be stable, and the training and verification accuracy of CNN model gradually increased and tended to be stable. The average loss rate of training samples determined by the CNN model was (22.35 ± 0.62) %, and the average loss rate of test samples determined by the CNN model was (26.41 ± 3.37) %. The average accuracy rate of training samples determined by the CNN model was (91.04 ± 2.11) %, and the average accuracy rate of test samples determined by the CNN model was (91.26 ± 1.02)%. Conclusion. A CNN model exhibits a high value in the cytological diagnosis of thyroid diseases which can be used for the cytological diagnosis of malignant thyroid tumor in the clinic.Ying RenYu HeLinghua CongHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Ying Ren Yu He Linghua Cong Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules
description	Objective. To investigate the application value of a deep convolutional neural network (CNN) model for cytological assessment of thyroid nodules. Methods. 117 patients with thyroid nodules who underwent thyroid cytology examination in the Affiliated People’s Hospital of Ningbo University between January 2017 and December 2019 were included in this study. 100 papillary thyroid cancer samples and 100 nonmalignant samples were collected respectively. The sample images were translated vertically and horizontally. Thus, 900 images were separately created in the vertical and horizontal directions. The sample images were randomly divided into training samples (n = 1260) and test samples (n = 540) at the ratio of 7 : 3 per the training sample to test sample. According to the training samples, the pretrained deep convolutional neural network architecture Resnet50 was trained and fine-tuned. A convolutional neural network-based computer-aided detection (CNN-CAD) system was constructed to perform full-length scan of the test sample slices. The ability of CNN-CAD to screen malignant tumors was analyzed using the threshold setting method. Eighty pathological images were collected from patients who received treatment between January 2020 and May 2020 and used to verify the value of CNN in the screening of malignant thyroid nodules as verification set. Results. With the number of iterations increasing, the training and verification loss of CNN model gradually decreased and tended to be stable, and the training and verification accuracy of CNN model gradually increased and tended to be stable. The average loss rate of training samples determined by the CNN model was (22.35 ± 0.62) %, and the average loss rate of test samples determined by the CNN model was (26.41 ± 3.37) %. The average accuracy rate of training samples determined by the CNN model was (91.04 ± 2.11) %, and the average accuracy rate of test samples determined by the CNN model was (91.26 ± 1.02)%. Conclusion. A CNN model exhibits a high value in the cytological diagnosis of thyroid diseases which can be used for the cytological diagnosis of malignant thyroid tumor in the clinic.
format	article
author	Ying Ren Yu He Linghua Cong
author_facet	Ying Ren Yu He Linghua Cong
author_sort	Ying Ren
title	Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules
title_short	Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules
title_full	Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules
title_fullStr	Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules
title_full_unstemmed	Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules
title_sort	application value of a deep convolutional neural network model for cytological assessment of thyroid nodules
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/af2cb688be9e49eca7fa5068d468d5a6
work_keys_str_mv	AT yingren applicationvalueofadeepconvolutionalneuralnetworkmodelforcytologicalassessmentofthyroidnodules AT yuhe applicationvalueofadeepconvolutionalneuralnetworkmodelforcytologicalassessmentofthyroidnodules AT linghuacong applicationvalueofadeepconvolutionalneuralnetworkmodelforcytologicalassessmentofthyroidnodules
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Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules

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