• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Enhanced Diagnostic of Pulmonary Embolism Detection using DenseNet and XGBoost

    View/Open
    Enhanced_Diagnostic_of_Pulmonary_Embolism_Detection_using_DenseNet_and_XGBoost.pdf (492.4Kb)
    Date
    2024
    Author
    Ahmad, Zahoor
    Al-Maadeed, Somaya Ali
    Khan, Muhammad Asif
    Metadata
    Show full item record
    Abstract
    Pulmonary embolism (PE) poses a significant medical challenge, often linked to severe morbidity and mortality rates. This study investigates the effectiveness of machine learning techniques in detecting pulmonary embolism through radiological images. Utilizing DenseNet121 convolutional neural networks and XGBoost classifiers, we analyze the RSNA Kaggle Pulmonary Embolism Dataset, encompassing diverse CT scans. Initial preprocessing involves standardizing image sizes and normalization, with a focus on CT scan image origins and Hounsfield Units (HU) augmentation. Features extracted using DenseNet121 are utilized for classification tasks via an XGBoost classifier. Comprehensive evaluation metrics including ROC curves, AUC scores, confusion matrices, and classification reports assess model performance. Results show promising mean accuracy rates (99.38%), sensitivity (99.40%), and specificity (99.40%), highlighting the potential of machine learning in assisting clinicians with precise PE predictions from thoracic CT scans. This research emphasizes the practicality of machine learning in enhancing PE diagnosis through radiological imaging, contributing to improved diagnostic precision and timely clinical interventions.
    DOI/handle
    http://dx.doi.org/10.1109/ICFTSS61109.2024.10691334
    http://hdl.handle.net/10576/60248
    Collections
    • Computer Science & Engineering [‎2429‎ items ]
    • QMIC Research [‎278‎ 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 | Send Feedback
    Contact Us | Send Feedback | 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 policiesUser guides FAQs

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

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video