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Classifying Breast Tumors in Digital Tomosynthesis by Combining Image Quality-Aware Features and Tumor Texture Descriptors

  • Dades identificatives

    Identificador: imarina:9385307
    Autors:
    Hassan, LoayAbdel-Nasser, MohamedSaleh, AdelPuig, Domenec
    Resum:
    Digital breast tomosynthesis (DBT) is a 3D breast cancer screening technique that can overcome the limitations of standard 2D digital mammography. However, DBT images often suffer from artifacts stemming from acquisition conditions, a limited angular range, and low radiation doses. These artifacts have the potential to degrade the performance of automated breast tumor classification tools. Notably, most existing automated breast tumor classification methods do not consider the effect of DBT image quality when designing the classification models. In contrast, this paper introduces a novel deep learning-based framework for classifying breast tumors in DBT images. This framework combines global image quality-aware features with tumor texture descriptors. The proposed approach employs a two-branch model: in the top branch, a deep convolutional neural network (CNN) model is trained to extract robust features from the region of interest that includes the tumor. In the bottom branch, a deep learning model named TomoQA is trained to extract global image quality-aware features from input DBT images. The quality-aware features and the tumor descriptors are then combined and fed into a fully-connected layer to classify breast tumors as benign or malignant. The unique advantage of this model is the combination of DBT image quality-aware features with tumor texture descriptors, which helps accurately classify breast tumors as benign or malignant. Experimental results on a publicly available DBT image dataset demonstrate that the proposed framework achieves superior breast tumor classification results, outperforming all existing deep learning-based methods.
  • Altres:

    Autor segons l'article: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Puig, Domenec
    Departament: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 2504-4990
    Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Paraules clau: Image quality assessment Image quality assessmen Deep learning Breast tomosynthesis Breast cancer classification
    Resum: Digital breast tomosynthesis (DBT) is a 3D breast cancer screening technique that can overcome the limitations of standard 2D digital mammography. However, DBT images often suffer from artifacts stemming from acquisition conditions, a limited angular range, and low radiation doses. These artifacts have the potential to degrade the performance of automated breast tumor classification tools. Notably, most existing automated breast tumor classification methods do not consider the effect of DBT image quality when designing the classification models. In contrast, this paper introduces a novel deep learning-based framework for classifying breast tumors in DBT images. This framework combines global image quality-aware features with tumor texture descriptors. The proposed approach employs a two-branch model: in the top branch, a deep convolutional neural network (CNN) model is trained to extract robust features from the region of interest that includes the tumor. In the bottom branch, a deep learning model named TomoQA is trained to extract global image quality-aware features from input DBT images. The quality-aware features and the tumor descriptors are then combined and fed into a fully-connected layer to classify breast tumors as benign or malignant. The unique advantage of this model is the combination of DBT image quality-aware features with tumor texture descriptors, which helps accurately classify breast tumors as benign or malignant. Experimental results on a publicly available DBT image dataset demonstrate that the proposed framework achieves superior breast tumor classification results, outperforming all existing deep learning-based methods.
    Àrees temàtiques: Engineering, electrical & electronic Engineering (miscellaneous) Computer science, interdisciplinary applications Computer science, artificial intelligence Artificial intelligence
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
    Identificador de l'autor: 0000-0002-1074-2441 0000-0002-0562-4205
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2504-4990/6/1/29
    Referència a l'article segons font original: Machine Learning And Knowledge Extraction. 6 (1): 619-641
    Referència de l'ítem segons les normes APA: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Puig, Domenec (2024). Classifying Breast Tumors in Digital Tomosynthesis by Combining Image Quality-Aware Features and Tumor Texture Descriptors. Machine Learning And Knowledge Extraction, 6(1), 619-641. DOI: 10.3390/make6010029
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.3390/make6010029
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2024
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Computer Science, Interdisciplinary Applications,Engineering (Miscellaneous),Engineering, Electrical & Electronic
    Image quality assessment
    Image quality assessmen
    Deep learning
    Breast tomosynthesis
    Breast cancer classification
    Engineering, electrical & electronic
    Engineering (miscellaneous)
    Computer science, interdisciplinary applications
    Computer science, artificial intelligence
    Artificial intelligence
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