Impact Factor: 7.883
Submit Paper
Volume 11, Issue 5 (May 2025)

Deep Learning Approach For Brain Tumor Classification, Segmentation And Detection

Impact Factor
7.883
Call For Paper
Volume 12 Issue 07

July 2026

Download Paper Format
Paper Format
Copyright Form
Copy Right Format
Under License Of
IJSART License
Creative Commons Attribution-NonCommercial
4.0 International License
Share on:

Author(s)

Dr. K. Srinivasan Sanjay Kumar A

Abstract

Brain Tumor Detection Remains A Critical Challenge In Medical Diagnostics. This Paper Presents A Comparative Analysis Of Classification-based, Segmentation-based, And Hybrid Deep Learning Approaches For Brain Tumor Diagnosis. The System Employs Image Processing And Convolutional Neural Networks (CNNs), Particularly The VGG16 Model, To Extract And Classify Features From MRI Scans. Tumor Stages And Regions Are Identified Using Segmentation Techniques, While Tumor Types Are Classified Using Support Vector Machines (SVM). Experimental Validation Using MRI Data From 70 Participants Revealed That The Hybrid Approach Achieved The Highest Balanced Accuracy Of 87.7%, Slightly Outperforming Classification-only (87.1%).

Keywords

Brain Tumor Detection CNN VGG16 Tumor Segmentation Watershed Algorithm Tumor Stage Prediction.

Paper ID

IJSARTV11I5103611

Publication Date

May 20, 2025

Research Area

CSA

Download Full Article

Submit Your Paper to IJSART

Join the global research community with IJSART. Submit your paper, share your work, and gain worldwide recognition!

Submit Your Paper