Brain Tumor Detection From Mri Scans Using Convolutional Neural Networks

• Author(s):

  Sarthak Rathore | Prof. Pankaj Raghuwanshi

• Keywords:

  Brain Tumor Detection, MRI, Deep Learning, Convolutional Neural Network, Medical Image Analysis, Artificial Intelligence.

• Abstract:

  Brain Tumors Are Among The Most Life-threatening Neurological Disorders That Require Early And Accurate Diagnosis To Improve Patient Survival And Treatment Outcomes. Magnetic Resonance Imaging (MRI) Is Widely Used For Detecting Brain Abnormalities Because It Provides High-resolution Images Of Brain Tissues. However, Manual Examination Of MRI Scans Is Time-consuming And Depends Heavily On The Expertise Of Radiologists, Which May Result In Diagnostic Errors Or Delays. In Recent Years, Artificial Intelligence (AI) And Deep Learning Techniques Have Significantly Improved The Field Of Medical Image Analysis By Providing Automated And Reliable Disease Detection Systems. This Research Presents A Convolutional Neural Network (CNN)-based Approach For Automated Brain Tumor Detection Using MRI Images. The Proposed Methodology Includes Dataset Collection, Image Preprocessing, CNN Model Development, Model Training, Testing, And Performance Evaluation. Image Preprocessing Techniques Such As Resizing, Normalization, And Data Augmentation Are Employed To Improve Image Quality And Enhance Model Learning. The CNN Architecture Automatically Extracts Important Features From MRI Images Without Requiring Manual Feature Engineering, Thereby Improving Classification Accuracy. The Proposed Model Classifies MRI Images Into Two Categories: Tumor And Non-Tumor. Performance Is Evaluated Using Standard Metrics Including Accuracy, Precision, Recall, F1-Score, Sensitivity, Specificity, And Confusion Matrix Analysis. Experimental Results Demonstrate That The CNN-based Approach Achieves High Classification Accuracy While Reducing Diagnostic Time And Improving Consistency. The Proposed System Can Serve As An Effective Computer-aided Diagnosis (CAD) Tool To Assist Radiologists In The Early Detection Of Brain Tumors And Support Clinical Decision-making.

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Other Details

• Paper id:

  IJSARTV12I7105788

• Published in:

  Volume: 12 Issue: 7 July 2026

• Publication Date:

  2026-07-24

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