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An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks.
Baccouche, Asma; Garcia-Zapirain, Begonya; Elmaghraby, Adel S.
Afiliación
  • Baccouche A; Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA. asma.baccouche@louisville.edu.
  • Garcia-Zapirain B; eVida Research Group, University of Deusto, 4800, Bilbao, Spain.
  • Elmaghraby AS; Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA.
Sci Rep ; 12(1): 12259, 2022 07 18.
Article en En | MEDLINE | ID: mdl-35851592
A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially into one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for (1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; (2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and (3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Mamografía Tipo de estudio: Diagnostic_studies / Prognostic_studies / Qualitative_research / Screening_studies Límite: Female / Humans Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Mamografía Tipo de estudio: Diagnostic_studies / Prognostic_studies / Qualitative_research / Screening_studies Límite: Female / Humans Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
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