ENHANCED BRAIN TUMOR DETECTION AND CLASSIFICATION USING VGG-16 WITH TRANSFER LEARNING ON MRI SCAN
K. JEYA CAROLIN AGNES
, G. PRINCE DEVARAJ
Deep learning, MRI imaging ,Convolutional neural networks, Brain tumor identification, VGG-16 model
Brain tumor detection is a critical task in medical imaging, where early and accurate diagnosis can significantly improve patient outcomes. This project presents a solution for automated brain tumor detection and classification using MRI scans. Leveraging the pre-trained VGG16 model with transfer learning, the system classifies brain tumors into three categories: Glioma,
Meningioma, Pituitary tumors. The model is trained using an augmented dataset and achieves high accuracy through optimization techniques like Adam.
The results include a comprehensive evaluation through metrics such as precision, recall, F1-score, and a confusion matrix, demonstrating the effectiveness of this approach. Future enhancements involve using more advanced architectures, segmentation, and real-world application in clinical environments.
"ENHANCED BRAIN TUMOR DETECTION AND CLASSIFICATION USING VGG-16 WITH TRANSFER LEARNING ON MRI SCAN", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.c427-c437, October-2024, Available :https://ijnrd.org/papers/IJNRD2410253.pdf
Volume 9
Issue 10,
October-2024
Pages : c427-c437
Paper Reg. ID: IJNRD_301478
Published Paper Id: IJNRD2410253
Downloads: 00020
Research Area: Science and Technology
Country: Tirunelveli, Tamilnadu, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
