• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • About QSpace
    • Vision & Mission
  • Help
    • Item Submission
    • Publisher policies
    • User guides
      • QSpace Browsing
      • QSpace Searching (Simple & Advanced Search)
      • QSpace Item Submission
      • QSpace Glossary
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Student Thesis & Dissertations
  • College of Engineering
  • Computing
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Student Thesis & Dissertations
  • College of Engineering
  • Computing
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    EXPLAINABLE BREAST CANCER DETECTION IN MAMMOGRAMS USING LIGHTWEIGHT EFFICIENTNET-B0 WITH GRAD-CAM AND LIME

    Thumbnail
    View/Open
    Noora Shifa_ OGS Approved Thesis.pdf (4.857Mb)
    Date
    2025-06
    Author
    SHIFA, NOORA
    Metadata
    Show full item record
    Abstract
    Accurate and interpretable breast cancer detection is essential for early diagnosis, yet many deep learning models remain impractical for real-world clinical deployment due to their complexity, resource demands, or lack of transparency. This study presents a comparative analysis of three CNN architectures, ResNet-50 (49.68M parameters), EfficientNet-B0 (20.36M), and EfficientNet-B7 (96.71M), to evaluate trade-offs between diagnostic performance, computational efficiency, and explainability for mammogram classification. EfficientNet-B0 achieved the highest accuracy (98.71%) and AUC (0.9996) on the MIAS dataset while requiring the fewest parameters. It also demonstrated strong generalizability on INbreast (93.32%) and DMID (94.43%) without dataset-specific fine-tuning. For interpretability, Grad-CAM and LIME visualisations were generated and qualitatively reviewed by a board-certified radiologist, who confirmed that the highlighted regionswere clinically meaningful and expressed a preference for Grad-CAM's contiguous heat-maps. These findings highlight EfficientNet-B0 as a lightweight and generalizable model, well-suited for scalable, real-world deployment in AI-assisted mammography, and underscore that robust accuracy, efficiency, and interpretability need not be mutually exclusive.
    DOI/handle
    http://hdl.handle.net/10576/67356
    Collections
    • Computing [‎112‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us
    Contact Us | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policies

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us
    Contact Us | QU

     

     

    Video