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Deep Learning-Driven Detection and Prediction of Brain Aneurysm: A CNN-Based Approach to Enhance Diagnosis through CT-Scan Imaging

  • Unique Paper ID: 174982
  • Volume: 11
  • Issue: 11
  • PageNo: 1209-1213
  • Abstract:
  • Brain aneurysms are severe cerebrovascular abnormalities that may rupture, leading to life-threatening complications like hemorrhagic stroke. Early detection is crucial to prevent fatal outcomes. Early diagnosis is crucial for effective treatment and patient survival. This study explores the application of deep learning, particularly Convolutional Neural Networks (CNNs), in detecting brain aneurysms using CT scan images. The developed CNN model classifies CT scans into aneurysm and non-aneurysm categories, leveraging a dataset of 81 aneurysm-positive and 300 aneurysm-negative images. Data augmentation techniques were implemented to address class imbalance. The proposed model demonstrated high classification accuracy, underscoring the potential of AI in assisting radiologists with early aneurysm detection. The paper further discusses model limitations, ethical considerations, and future research directions for advancing AI-driven medical diagnostics.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 1209-1213

Deep Learning-Driven Detection and Prediction of Brain Aneurysm: A CNN-Based Approach to Enhance Diagnosis through CT-Scan Imaging

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