A Deep Learning Approach for Detection of Brain Tumour Detection using Softmax Classifier and Backpropagation

Registration ID: IJNRD_324676

Published ID: IJNRD2605245

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V.Mathumitha, V.Nagul Meera Reddy, U.Bhaskar Akhil

Brain Tumour classification, Deep Learning,Machine Learning,Convolutional Nueral Networks.

The rapid advancement of medical imaging has necessitated the development of automated systems capable of assisting radiologists in the high-stakes task of brain tumor diagnosis. This project presents an AI-powered brain tumor detection and classification system utilizing a deep learning framework, specifically designed to identify and categorize malignancies such as gliomas, meningiomas, and pituitary tumors from Magnetic Resonance Imaging (MRI) scans. By implementing a multi-stage pipeline—encompassing automated skull stripping, intensity normalization, and a high-performance Convolutional Neural Network (CNN)—the system effectively standardizes raw medical data and extracts complex pathological features. The core architecture was trained and validated on diverse datasets to ensure high sensitivity and specificity, ultimately achieving a diagnostic accuracy rate of 96.4%. This automated approach significantly reduces the time required for initial screening, addressing the challenges of human fatigue and subjective interpretation in clinical workflows. Beyond the technical accuracy of the classifier, the system prioritizes transparency and clinical utility through the integration of explainable AI (XAI) components. Utilizing Class Activation Mapping (CAM), the framework generates real-time heatmaps that highlight the specific regions of the brain influencing the model’s diagnostic decision, thereby allowing medical professionals to visually verify the findings. The project also features a secure web-based user interface that facilitates seamless data ingestion and generates comprehensive clinical reports, including tumor types and confidence scores. Experimental results demonstrate that the system maintains robust performance across various MRI hardware vendors and sub-optimal imaging conditions, achieving an inference latency of less than 20ms. Consequently, this research provides a scalable and reliable decision-support tool that bridges the gap between raw data and actionable oncology insights, potentially democratizing high-quality neurological care in underserved medical environments.

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V.Mathumitha, V.Nagul Meera Reddy, & U.Bhaskar Akhil (May-2026). A Deep Learning Approach for Detection of Brain Tumour Detection using Softmax Classifier and Backpropagation. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), c430-c445. https://ijnrd.org/papers/IJNRD2605245.pdf

Volume 11 Issue 5, May-2026

Pages : c430-c445

Paper Reg. ID: IJNRD_324676

Published Paper Id: IJNRD2605245

Research Area: Other area not in list

Author Type: Indian Author

Country: chennai, Chengalpattu district, TamilNadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2605245.pdf

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