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@article{174982,
        author = {Dr. P. VEERESH and D UDAY KRIAN and KENCHUGUNDU MALLIKARJUNA and SHAIK AKBAR BASHA and GORLA SRICHARAN and CHAKKA VENKATA SAI ABHIRAM},
        title = {Deep Learning-Driven Detection and Prediction of Brain Aneurysm: A CNN-Based Approach to Enhance Diagnosis through CT-Scan Imaging},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1209-1213},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174982},
        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.},
        keywords = {Brain Aneurysm, Deep Learning, Convolutional Neural Network, CT Scan, Medical Imaging, AI in Healthcare.},
        month = {April},
        }