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Brain Tumor and Blockage Detection System Using Deep Learning

Udar Arati Ajay, Londhe Pranali Babasaheb, Khemnar K C, Kadam Rutuja Laxman, Shaikh Mahek Javed, Maniyar A A

Abstract


Brain tumors and cerebral blockages (which lead to strokes) are life-threatening conditions requiring immediate and accurate diagnosis. Manual evaluation of MRI scans by radiologists is slow and subjective. This paper proposes an automated deep learning system using a Convolutional Neural Network (CNN) to classify brain MRI images into three categories: Tumor, Blockage, or Normal. The system preprocesses images (grayscale, resizing, and normalization), extracts spatial features through convolutional and pooling layers, and outputs a prediction with confidence. The method reduces diagnostic time, eliminates manual feature engineering, and provides consistent results. Experimental evaluation on a mixed MRI dataset shows an expected accuracy of over 95%, demonstrating its potential as a clinical decision support tool.


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