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@article{205281,
        author = {Mrs. A. Bamila Rachel and Ms. M. Karpoora Jothi},
        title = {Web-Based Brain Tumor Classification System using MRI Images and ResNet-18},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5968-5974},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205281},
        abstract = {Brain tumor is one of the most life-threatening neurological conditions, requiring early and accurate detection for effective clinical intervention. Manual analysis of MRI scans is time-consuming, costly, and susceptible to human error. This paper proposes a brain tumor classification system based on ResNet-18 to perform the task of automatic classification of brain tumors, including four classes for MRI: Glioma, Meningioma, Pituitary Tumor, and No Tumor. The model learns from 7,200 MRI images using transfer learning with data augmentation and achieves 99.25% training accuracy and 89.56% test accuracy. The trained model is deployed as a Flask-based web application for real-time MRI upload, instantaneous tumor classification, treatment recommendation, confidence scoring, and Canvas API-based tumor region highlighting.},
        keywords = {Brain tumor classification, MRI, ResNet-18, transfer learning, deep learning, Flask web application, convolutional neural network, medical image analysis, treatment recommendation},
        month = {June},
        }