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@article{177414,
        author = {Omkar.Pacha and Mr. Yerrabathana Guravaiah},
        title = {Brain Tumor Detection using transfer learning: A Breakthrough in Medical Imaging},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {549-554},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177414},
        abstract = {Early detection of brain tumors is critical for improving patient outcomes and survival rates. Traditional diagnostic techniques, while effective, are often resource-intensive, time-consuming, and subject to human interpretation. This research introduces a cutting-edge approach to brain tumor detection using transfer learning, a deep learning technique that leverages pre-trained convolutional neural networks (CNNs) for accurate and efficient medical image classification. By fine-tuning models such as VGG16, ResNet50, and EfficientNet on MRI datasets, the proposed system achieves high accuracy in distinguishing between tumor and non-tumor images. The integration of transfer learning significantly reduces the need for large training datasets and accelerates model convergence. Experimental evaluations demonstrate the system's potential as a supportive diagnostic tool, providing consistent and scalable analysis to assist radiologists and medical professionals.},
        keywords = {SVM, KNN, CNN, Brain Dataset.},
        month = {May},
        }