Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.
The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 4; pp. 858 - 869 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2020
|
| 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=146122211&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146122211 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2020 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146122211 143913577 146122211 146122211 10.1007/s10278-020-00330-4 146122211 ppf: 858 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning. aug: au: Zeiser, Felipe André da Costa, Cristiano André Zonta, Tiago Marques, Nuno M. C. Roehe, Adriana Vial Moreno, Marcelo da Rosa Righi, Rodrigo affil: Software Innovation Laboratory – SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos – Unisinos, Av. Unisinos 950, 93022-000, São Leopoldo, Brazil sug: subj: Deep Learning Mammography Neural Networks (Computer) Breast Neoplasms Software Design Diagnosis, Computer Assisted Sensitivity and Specificity Human ab: The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|