Repositori institucional URV
| Autor/es de la URV: | Abdelnasser Mohamed Mahmoud, Mohamed / Moreno Ribas, Antonio / Puig Valls, Domènec Savi |
| Autor según el artículo: | Abdel-Nasser, M; Moreno, A; Puig, D |
| Direcció de correo del autor: | mohamed.abdelnasser@urv.cat mohamed.abdelnasser@urv.cat antonio.moreno@urv.cat antonio.moreno@urv.cat domenec.puig@urv.cat domenec.puig@urv.cat |
| Identificador del autor: | 0000-0002-1074-2441 0000-0002-1074-2441 0000-0003-3945-2314 0000-0003-3945-2314 0000-0002-0562-4205 0000-0002-0562-4205 |
| Año de publicación de la revista: | 2019-01-01 |
| Tipo de publicación: | Journal Publications |
| ISSN: | 08834989 |
| e-ISSN: | 0883-4989 |
| Referencia de l'ítem segons les normes APA: | Abdel-Nasser, M; Moreno, A; Puig, D (2019). Breast cancer detection in thermal infrared images using representation learning and texture analysis methods. Electronics, 8(1), 100-. DOI: 10.3390/electronics8010100 |
| Referencia al articulo segun fuente origial: | Electronics. 8 (1): 100- |
| Resumen: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature. |
| DOI del artículo: | 10.3390/electronics8010100 |
| Enlace a la fuente original: | https://www.mdpi.com/2079-9292/8/1/100 |
| 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: | Signal processing Physics, applied Hardware and architecture Engineering, electrical & electronic Engenharias iv Electrical and electronic engineering Control and systems engineering Computer science, information systems Computer networks and communications Biotecnología |
| Palabras clave: | Thermography Thermal infrared images Texture analysis Statistics Representation learning Mammography Machine learning Features Database Computer-aided diagnosis systems Classification Breast cancer |
| Entidad: | Universitat Rovira i Virgili |
| Fecha de alta del registro: | 2026-05-09 |
| Volumen de revista: | 8 |
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