Autor segons l'article: Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Puig, Domenec
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
Paraules clau: Breast cancer classification Brest cancer classification Computer vision Deep learning Digital breast tomosynthesis Support vector machin Support vector machine
Resum: 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.
Àrees temàtiques: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
Identificador de l'autor: 0000-0002-0562-4205 0000-0002-1074-2441
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://ebooks.iospress.nl/doi/10.3233/FAIA220348
Referència a l'article segons font original: Frontiers In Artificial Intelligence And Applications. 356 269-278
Referència 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 Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.3233/FAIA220348
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Proceedings Paper
Repositori URV