Articles producció científica> Enginyeria Informàtica i Matemàtiques

Breast cancer detection in thermal infrared images using representation learning and texture analysis methods

  • Identification data

    Identifier: imarina:4225097
    Handle: https://hdl.handle.net/20.500.11797/imarina4225097
    Authors:
    Abdel-Nasser, MohamedMoreno, AntonioPuig, Domenec
    Abstract:
    © 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.

  • Others:

    Author, as appears in the article.: Abdel-Nasser, Mohamed; Moreno, Antonio; Puig, Domenec
    Department: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 0883-4989
    URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Moreno Ribas, Antonio / Puig Valls, Domènec Savi
    Keywords: Thermography Thermal infrared images Texture analysis Statistics Representation learning Mammography Machine learning Features Database Computer-aided diagnosis systems Classification Breast cancer
    Abstract: © 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 08834989
    Author's mail: mohamed.abdelnasser@urv.cat antonio.moreno@urv.cat domenec.puig@urv.cat
    Author identifier: 0000-0002-1074-2441 0000-0003-3945-2314 0000-0002-0562-4205
    Record's date: 2024-10-12
    Journal volume: 8
    Papper version: info:eu-repo/semantics/publishedVersion
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Electronics. 8 (1): 100-
    APA: Abdel-Nasser, Mohamed; Moreno, Antonio; Puig, Domenec (2019). Breast cancer detection in thermal infrared images using representation learning and texture analysis methods. Electronics, 8(1), 100-. DOI: 10.3390/electronics8010100
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2019
    Publication Type: Journal Publications

  • Keywords:

    Computer Networks and Communications,Computer Science, Information Systems,Control and Systems Engineering,Electrical and Electronic Engineering,Engineering, Electrical & Electronic,Hardware and Architecture,Physics, Applied,Signal Processing
    Thermography
    Thermal infrared images
    Texture analysis
    Statistics
    Representation learning
    Mammography
    Machine learning
    Features
    Database
    Computer-aided diagnosis systems
    Classification
    Breast cancer
    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

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