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@article{204546,
        author = {Pravitha V. Devan and Divya V},
        title = {A Comparative Study of Deep Learning Architectures for Early Brain Tumor Detection Using Medical Imaging},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {240-244},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204546},
        abstract = {Early detection of brain tumors is critical for improving patient survival rates and treatment outcomes. With the advancement of medical imaging technologies such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), large volumes of diagnostic data are generated, necessitating efficient and accurate automated analysis. Deep learning techniques, particularly Convolutional Neural Networks (CNNs) and their variants, have demonstrated remarkable performance in medical image analysis. This review paper presents a comparative study of various deep learning architectures employed for early brain tumor detection using medical imaging. It evaluates models based on accuracy, computational efficiency, robustness, and interpretability. The study also highlights current challenges, limitations, and future research directions in this domain.},
        keywords = {Brain Tumor Detection, Deep Learning, CNN, MRI, Medical Imaging, Comparative Study.},
        month = {June},
        }