Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging.
Objective. The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNN...
| Published in: | BioMed Research International pp. 1 - 11 |
|---|---|
| Main Authors: | , , , |
| Format: | diagnostic images pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
1/10/2020
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141157570&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141157570 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/10/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141157570 141157570 141157570 10.1155/2020/1763803 141157570 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging. aug: au: Liang, Xiaowen Yu, Jinsui Liao, Jianyi Chen, Zhiyi affil: Department of Ultrasound Medicine, The Third Affiliated Hospital of Guangzhou Medical University, 63 Duobao Road, Guangzhou, 510000 Guangdong, China sug: subj: Computer-Aided Design Lymph Nodes Ultrasonography Breast Ultrasonography Thyroid Gland Ultrasonography Breast Neoplasms Diagnosis Thyroid Neoplasms Diagnosis Sensitivity and Specificity Human Prospective Studies ROC Curve Predictive Value of Tests Probability ab: Objective. The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods. The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results. Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions. The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|