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

Omkar.Pacha; Mr. Yerrabathana Guravaiah

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

Authors: Omkar.Pacha, Mr. Yerrabathana Guravaiah

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

Keywords: SVM KNN CNN Brain Dataset.

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.

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Cite This Article

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

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

Available:https://ijirt.org/Article?manuscript=177414

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