Author, as appears in the article.: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Puig, Domenec
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
Keywords: Breast cancer classification Brest cancer classification Computer vision Deep learning Digital breast tomosynthesis Support vector machin Support vector machine
Abstract: 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.
Thematic Areas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
Author identifier: 0000-0002-0562-4205 0000-0002-1074-2441
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://ebooks.iospress.nl/doi/10.3233/FAIA220348
Papper original source: Frontiers In Artificial Intelligence And Applications. 356 269-278
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
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.3233/FAIA220348
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Proceedings Paper