Differentiation of the Follicular Neoplasm on the Gray-Scale US by Image Selection Subsampling along with the Marginal Outline Using Convolutional Neural Network.
We conducted differentiations between thyroid follicular adenoma and carcinoma for 8-bit bitmap ultrasonography (US) images utilizing a deep-learning approach. For the data sets, we gathered small-boxed selected images adjacent to the marginal outline of nodules and applied a convolutional neural ne...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 14 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/19/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126852067&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126852067 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/19/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 126852067 126852067 126852067 10.1155/2017/3098293 126852067 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Differentiation of the Follicular Neoplasm on the Gray-Scale US by Image Selection Subsampling along with the Marginal Outline Using Convolutional Neural Network. aug: au: Seo, J.-K. Kim, Y.-J. Kim, K.-G. Shin, Ilah Shin, Jung Hee Kwak, J.-Y. affil: Department of Biomedical Engineering, School of Medicine, Gachon University, Gyeonggi-do, Republic of Korea sug: subj: Thyroid Neoplasms Ultrasonography Neural Networks (Computer) Utilization Diagnosis, Differential Descriptive Statistics Data Analysis Software ROC Curve South Korea Sample Size Adenoma Diagnosis Carcinoma Diagnosis Human Female Male Middle Age Middle Aged: 45-64 years Female Male ab: We conducted differentiations between thyroid follicular adenoma and carcinoma for 8-bit bitmap ultrasonography (US) images utilizing a deep-learning approach. For the data sets, we gathered small-boxed selected images adjacent to the marginal outline of nodules and applied a convolutional neural network (CNN) to have differentiation, based on a statistical aggregation, that is, a decision by majority. From the implementation of the method, introducing a newly devised, scalable, parameterized normalization treatment, we observed meaningful aspects in various experiments, collecting evidence regarding the existence of features retained on the margin of thyroid nodules, such as 89.51% of the overall differentiation accuracy for the test data, with 93.19% of accuracy for benign adenoma and 71.05% for carcinoma, from 230 benign adenoma and 77 carcinoma US images, where we used only 39 benign adenomas and 39 carcinomas to train the CNN model, and, with these extremely small training data sets and their model, we tested 191 benign adenomas and 38 carcinomas. We present numerical results including area under receiver operating characteristic (AUROC). pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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