Year: 2025
Type: article
Abstract: — Brain tumors are among the most Life-threating and complicated diseases to improve treatment outcomes. Conventional diagnostic approaches radiologists’ expertise. This research introduces an automated framework for detecting brain tumors, utilizing the capabilities of VGG16, a predefined convolutional neural network model, variability and delays in decision-making, fine-tuned via transfer learning on MRI brain scan images. Our methodology emphasizes efficient data preprocessing, robust feature extraction, and a lightweight classification head tailored for binary classification. Experimental results reveal exceptional classification accuracy, validating the model's applicability in prediction effectiveness. This research highlights the transformative power of deep learning in augmenting diagnostic workflows and enabling scalable, real-time medical support. Keywords- Brain tumor, Deep learning, VGG16, MRI, Image based neural network, Cross - domain learning, Biomedical image analysis.
DOI: 10.55041/ijsrem48767
Author: Aishwarya S Gamanagatti
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4410739481