International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Detection of Brain Tumor using Neural Networks |
|---|---|
| ABSTRACT | Brain tumor detection from magnetic resonance imaging (MRI) plays a critical role in neuro- oncological diagnosis and treatment planning. This paper presents an automated brain tumor detection framework based on a fine-tuned ResNet50 deep convolutional neural network, integrated with Gradient-weighted Class Activation Mapping (Grad-CAM) to provide explainable decisions. The system classifies MRI scans into four categories— glioma, meningioma, pituitary tumor, and no tumor—and is deployed through a Streamlit-based web interface featuring secure authentication and encrypted PDF report generation. Experimental evaluation indicates stable learning behavior, high classification performance, and clinically meaningful Grad-CAM visualizations, suggesting that the proposed framework is suitable for real- world decision support. The system incorporates bcrypt hashing for secure doctor authentication and Fernet symmetric encryption to safeguard diagnostic PDF reports, ensuring privacy and integrity of medical data. |
| AUTHOR | PRIYA R SATWIK, PUNYA D, RESHMA S, SANJANA M H, PROF. CHIDANANDAN V Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312039 |
| pdf/39_Detection of Brain Tumor using Neural Networks.pdf | |
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