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@article{191017,
        author = {Mahalakshmi K S and Kavitha C R and Chaithanya M V and Deeksha Padmanabha Jain},
        title = {Identification and Classification of Glioblastoma MRI Images Using RES-UNIT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4560-4564},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191017},
        abstract = {Brain tumours are some of the most serious neurological conditions, with glioblastoma being the most malignant, aggressive, and lethal because of their rapid growth rate. For successful treatment strategies as well as survival rates of patients, early and correct diagnoses of the condition are necessary. Magnetic Resonance Imaging is currently in wide use for the diagnosis of brain tumours. The observation of Magnetic Resonance Imaging is manually time-consuming with possibilities of human errors. This paper proposes an automated deep learning-based glioblastoma MRI image identification and classification system based on Residual Unit Network (ResUnitNet). The proposed approach can classify glioblastoma MRI images for glioma, meningioma, pituitary tumours, and other tumours. Additionally, to explain and better understand the classification result, Grad-CAM is involved to highlight the impact region. A web application based on Flask tool development has been implemented for glioblastoma tumour classification, confidence level, illustration of the impact region, calculation of glioblastoma tumour impact areas, and clinical staging.},
        keywords = {Glioblastoma Detection, Brain Tumour Classification, ResUnitNet, Deep Learning, MRI Analysis.},
        month = {January},
        }