Brain Tumor Detection using transfer learning: A Breakthrough in Medical Imaging

  • Unique Paper ID: 177414
  • Volume: 11
  • Issue: 12
  • PageNo: 549-554
  • 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.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@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},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 549-554

Brain Tumor Detection using transfer learning: A Breakthrough in Medical Imaging

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