Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.
The purpose of this study is to test the feasibility for deep CNN–based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature–based machine learning methods were also implemented in this study as baseline and for comparison study. In this r...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1314 - 1323 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2023
|
| 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=169808794&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808794 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808794 162466068 169808794 169808794 10.1007/s10278-023-00798-w 169808794 ppf: 1314 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics. aug: au: Qi, Longxiu Lu, Xing Shen, Hailin Gao, Qilei Han, Zhigang Zhu, Jianguo Meng, You Wang, Linhua Chen, Shuangqing Li, Yonggang affil: Department of Radiology, The First Affiliated Hospital of Soochow University, Jiangsu Province, 215006, Suzhou City, Jiangsu Province, China sug: subj: Mammography Artificial Intelligence Neural Networks (Computer) Machine Learning Breast Neoplasms Classification Breast Neoplasms Radiography Human Comparative Studies Retrospective Design Software Funding Source ab: The purpose of this study is to test the feasibility for deep CNN–based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature–based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN–based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN–based AI methods, while radiomic feature–based machine learning methods achieved inferior classification results. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|