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@article{180413,
        author = {Vaibhav Sapate and Vidya Shinde and Anjali Jagtap and Vaishnavi Madalapure and Dr. Sanjay Ganorkar},
        title = {DETECTION OF BRAIN TUMORS FROM MRI IMAGES USING IMAGE PROCESSING AND MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1640-1644},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180413},
        abstract = {Brain tumor detection through MRI analysis plays a pivotal role in neuro-oncology, demanding both precision and efficiency. Manual diagnosis, although widely practiced, is often limited by subjectivity and variability. To address these challenges, this project presents an automated brain tumor classification system leveraging deep learning and transfer learning. The proposed pipeline incorporates advanced image preprocessing techniques such as cropping, denoising, and normalization, followed by data augmentation to address class imbalance. ResNet152, a state-of-the-art convolutional neural network, is fine-tuned using MRI images to classify tumors into four categories: glioma, meningioma, pituitary, and no tumor. The model achieves high classification accuracy, validated through multiple metrics, and demonstrates significant potential as a decision-support tool in clinical settings.},
        keywords = {Brain Tumor, Image Processing, MRI Images, ResNet 152, Deep Learning},
        month = {June},
        }