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@article{176129,
        author = {Ms. Preeti Namdeo},
        title = {Automated Brain Tumor Detection and Segmentation Using ResUNet Architecture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7899-7902},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176129},
        abstract = {Brain tumor detection and segmentation play a vital role in clinical diagnosis and treatment planning. This research presents a deep learning-based approach a two-stage deep learning approach using ResUNet — a hybrid architecture combining ResNet-50’s feature extraction capabilities with U-Net’s segmentation efficiency — for automated brain tumor detection and precise localization.  The study utilizes a publicly available Kaggle dataset comprising annotated MRI scans. The process begins with training a binary classification model to detect the presence of a tumor, followed by a segmentation model designed to localize the tumor region. The models are evaluated using standard metrics such as accuracy, cross-entropy loss, and segmentation performance indicators like Dice Similarity Coefficient. Experimental results demonstrate that the ResUNet model achieves promising performance in accurately segmenting tumor regions, contributing toward more efficient and reliable clinical support systems.},
        keywords = {},
        month = {May},
        }