IDEAS home Printed from https://ideas.repec.org/a/prg/jnlaip/v2024y2024i2id245p213-233.html
   My bibliography  Save this article

Deep Neural Network-Based Model for Breast Cancer Lesion Diagnosis in Mammography Images

Author

Listed:
  • Mohamed Amine Yakoubi
  • Nada Khiari
  • Amine Khiari
  • Ahlem Melouah

Abstract

Deep learning has made identifying breast cancer lesions in mammography images an easy task in modern medicine, which has helped improve the diagnosis efficiency, sensitivity and accuracy by precisely identifying breast cancer from mammography images, contributing to timely detection and maintaining consistent performance. This paper presents the steps and strategies to develop a deep learning (DL) model to detect lesions in mammography images, based on U-Net architecture for precise segmentation, which has been developed for biomedical image segmentation, and incorporating ResNet34 as its encoder to extract features. Next, we employ the FastAI library, which simplifies and accelerates the model training tasks. For the data, studies and available resources lead us to INbreast, which is built with full-field digital mammograms contrary to other digitized mammograms. We obtained a high accuracy of 98% on the INbreast database, which is very challenging compared to state-of-the-art results.

Suggested Citation

  • Mohamed Amine Yakoubi & Nada Khiari & Amine Khiari & Ahlem Melouah, 2024. "Deep Neural Network-Based Model for Breast Cancer Lesion Diagnosis in Mammography Images," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2024(2), pages 213-233.
    • Handle: RePEc:prg:jnlaip:v:2024:y:2024:i:2:id:245:p:213-233
    DOI: 10.18267/j.aip.245
    as

    Download full text from publisher

    File URL: http://aip.vse.cz/doi/10.18267/j.aip.245.html
    Download Restriction: free of charge
    File URL: http://aip.vse.cz/doi/10.18267/j.aip.245.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.18267/j.aip.245?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prg:jnlaip:v:2024:y:2024:i:2:id:245:p:213-233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.