Abstract :

Brain Tumor Detection Is One Of The Most Critical Tasks In The Field Of Medical Image Analysis, As Early And Accurate Identification Of Tumors Can Significantly Improve Patient Outcomes. Traditional Manual Diagnosis Through MRI Scans Is Time-consuming And Prone To Human Error. To Overcome These Limitations, Machine Learning (ML) And Deep Learning (DL) Techniques Have Emerged As Powerful Tools For Automated And Efficient Brain Tumor Detection. This Study Presents A Hybrid Approach That Integrates Both ML And DL Algorithms To Accurately Classify And Detect Brain Tumors From MRI Images. Preprocessing Methods Such As Image Normalization, Noise Reduction, And Segmentation Are Applied To Enhance Image Quality. Machine Learning Models Like Support Vector Machine (SVM) And Random Forest Are Used For Feature-based Classification, While Deep Learning Architectures Such As Convolutional Neural Networks (CNN) Are Employed For End-to-end Image Analysis And Feature Extraction. The Proposed System Improves Detection Accuracy, Reduces False Positives, And Enhances Diagnostic Efficiency Compared To Traditional Methods. Experimental Results Demonstrate That Deep Learning Models, Particularly CNNs, Outperform Conventional ML Models In Terms Of Precision, Recall, And Overall Accuracy. This Research Highlights The Potential Of Combining Machine Learning And Deep Learning For Reliable, Automated Brain Tumor Detection, Ultimately Aiding Radiologists In Faster And More Accurate Medical Diagnosis.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

141-146

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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