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Breast Tumor Classification in Digital Tomosynthesis Based on Deep Learning Radiomics

  • Datos identificativos

    Identificador: imarina:9385564
    Handle: https://hdl.handle.net/20.500.11797/imarina9385564
    Autores:
    Hassan, LoayAbdel-Nasser, MohamedSaleh, AdelPuig, Domenec
    Resumen:
    Breast cancer is the most frequently diagnosed cancer in women globally. Early and accurate detection and classification of breast tumors are critical in improving treatment strategies and increasing the patient survival rate. Digital breast tomosynthesis (DBT) is an advanced form of mammography that aids better in the early detection and diagnosis of breast disease. This paper proposes a breast tumor classification method based on analyzing and evaluating the performance of various of the most innovative deep learning classification models in cooperation with a support vector machine (SVM) classifier for a DBT dataset. Specifically, we study the ability to use transfer learning from non-medical images to classify tumors in unseen DBT medical images. In addition, we utilize the fine-tuning technique to improve classification accuracy.

  • Otros:

    Autor según el artículo: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Puig, Domenec
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Palabras clave: Breast cancer classification Brest cancer classification Computer vision Deep learning Digital breast tomosynthesis Support vector machin Support vector machine
    Resumen: Breast cancer is the most frequently diagnosed cancer in women globally. Early and accurate detection and classification of breast tumors are critical in improving treatment strategies and increasing the patient survival rate. Digital breast tomosynthesis (DBT) is an advanced form of mammography that aids better in the early detection and diagnosis of breast disease. This paper proposes a breast tumor classification method based on analyzing and evaluating the performance of various of the most innovative deep learning classification models in cooperation with a support vector machine (SVM) classifier for a DBT dataset. Specifically, we study the ability to use transfer learning from non-medical images to classify tumors in unseen DBT medical images. In addition, we utilize the fine-tuning technique to improve classification accuracy.
    Áreas temáticas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
    Identificador del autor: 0000-0002-0562-4205 0000-0002-1074-2441
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Referencia al articulo segun fuente origial: Frontiers In Artificial Intelligence And Applications. 356 269-278
    Referencia de l'ítem segons les normes APA: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Puig, Domenec (2022). Breast Tumor Classification in Digital Tomosynthesis Based on Deep Learning Radiomics. Amsterdam: IOS Press
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Proceedings Paper

  • Palabras clave:

    Artificial Intelligence
    Breast cancer classification
    Brest cancer classification
    Computer vision
    Deep learning
    Digital breast tomosynthesis
    Support vector machin
    Support vector machine
    Artificial intelligence
    Ciências agrárias i
    Comunicació i informació
    Engenharias iii
    Engenharias iv
    General o multidisciplinar
    Información y documentación
    Interdisciplinar
    Medicina ii

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