Repositori institucional URV
| Autor/es de la URV: | Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi |
| Autor según el artículo: | Hassan, Loay; Abdel-Nasser, Mohamed; Saleh, Adel; Puig, Domenec |
| Direcció de correo del autor: | domenec.puig@urv.cat mohamed.abdelnasser@urv.cat |
| Identificador del autor: | 0000-0002-0562-4205 0000-0002-1074-2441 |
| Año de publicación de la revista: | 2022 |
| Tipo de publicación: | Proceedings Paper |
| 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 |
| Referencia al articulo segun fuente origial: | Frontiers In Artificial Intelligence And Applications. 356 269-278 |
| 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. |
| DOI del artículo: | 10.3233/FAIA220348 |
| Enlace a la fuente original: | https://ebooks.iospress.nl/doi/10.3233/FAIA220348 |
| Versión del articulo depositado: | info:eu-repo/semantics/publishedVersion |
| Acceso a la licencia de uso: | https://creativecommons.org/licenses/by/3.0/es/ |
| Departamento: | Enginyeria Informàtica i Matemàtiques |
| URL Documento de licencia: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Áreas temáticas: | Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii |
| Palabras clave: | Breast cancer classification Brest cancer classification Computer vision Deep learning Digital breast tomosynthesis Support vector machin Support vector machine |
| Entidad: | Universitat Rovira i Virgili |
| Fecha de alta del registro: | 2024-10-12 |
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